JP7683239B2 - MEASUREMENT METHOD, MEASUREMENT DEVICE, MEASUREMENT SYSTEM, AND MEASUREMENT PROGRAM - Google Patents

Description

The present invention relates to a measurement method, a measurement device, a measurement system, and a measurement program.

Patent Document 1 describes a displacement acquisition device that includes a static component storage unit that stores a time series of static components, which are components that do not depend on the motion of a bridge girder from the time series of displacement of the bridge girder caused by the passage of a railway vehicle; a displacement detection unit that detects a time series of displacement of the girder of the bridge to be measured based on at least one of acceleration measurement values or speed measurement values of the girder of the bridge to be measured caused by the passage of the railway vehicle to be measured; a dynamic component extraction unit that extracts a time series of dynamic components, which are the remaining components after removing static components that may contain errors, from the time series of displacement detected by the displacement detection unit; a static component acquisition unit that acquires the time series of static components from the static component storage unit; and a synthesis unit that synthesizes the time series of dynamic components extracted by the dynamic component extraction unit and the time series of static components acquired by the static component acquisition unit.

According to the displacement acquisition device described in Patent Document 1, it is possible to obtain a displacement time series free of errors by removing static components that may contain errors from the detected time series of displacement of the girder and replacing them with stored static components.

JP 2009-237805 A

However, in the displacement acquisition device described in Patent Document 1, the accuracy of the obtained displacement time series is greatly affected by the similarity between the static components included in the detected girder displacement time series and the stored static components, so if the accuracy of the similarity is insufficient, the accuracy of the displacement time series may decrease. In addition, in the displacement acquisition device described in Patent Document 1, if the static components included in the displacement time series at the time of measurement change due to environmental changes, etc., there is no means to recognize the deviation between the static components and the stored static components, and it is not possible to know that there is a problem with the displacement accuracy. In addition, the displacement acquisition device described in Patent Document 1 must store static component data for each railway vehicle classification and each bridge classification, and the data must be acquired and updated, which makes the configuration complex and makes it difficult to reduce costs. Therefore, a method for reducing errors without preparing information for reducing errors, such as static component data, is desired.

One aspect of the measurement method according to the present invention is to
a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

Another aspect of the measurement method according to the present invention is to
a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced;
a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data in the second section, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
The method includes a measurement data generating process for generating measurement data by using the first section as the vibration component reduced data, adding the drift noise reduced data, the correction data and the vibration component data in the second section, and using the third section as the vibration component reduced data.

One aspect of the measuring device according to the present invention is
a low-pass filter processing unit that performs low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing unit that generates drift noise reduced data by high-pass filtering the vibration component reduced data to reduce the drift noise;
a correction data estimation unit that estimates correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating unit that generates vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
and a measurement data generating unit that generates measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

One aspect of the measurement system according to the present invention is
An embodiment of the measuring device;
An observation device for observing an observation point,
The target data is data based on observation data obtained by the observation device.

One aspect of the measurement program according to the present invention is
a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

FIG. 1 is a diagram showing an example of the configuration of a measurement system. 2 is a cross-sectional view of the upper structure of FIG. 1 taken along line AA. 4 is a diagram illustrating acceleration detected by an acceleration sensor. 1 is a diagram showing the relationship between frequency characteristics F{M _d (k)}, F{M(k)}, and F{e(k)}. FIG. 2 is a diagram showing the relationship between frequency characteristics F{M _s (k)}, F{M(k)}, and F{e(k)}. FIG. 13 is a diagram showing frequency characteristics F{MV(k)}. 1 is a diagram showing the relationship between frequency characteristics F{ _Ms (k)}, F{ _fHP ( _Ms (k))}, and F{ _fLP ( _Ms (k))}. FIG. FIG. 13 is a diagram showing the relationship between frequency characteristics F{ _Ms '(k)}, F{ _fHP ( _Ms (k))}, and F{ _ALP ( _fHP ( _Ms (k)))}. FIG. 2 is a diagram showing the relationship between frequency characteristics F{M _d '(k)}, F{M _s '(k)}, and F{M _v (k)}. FIG. 13 is a diagram showing data M _s (k) which is a unit pulse waveform. FIG. 13 is a diagram showing data f _LP (M _s (k)) obtained by subjecting data M _s (k) to a low-pass filter process. FIG. 13 is a diagram showing data f _HP (M _s (k)) obtained by high-pass filtering data M _s (k). FIG. 13 is a diagram showing an example of target data U(k). FIG. 13 is a diagram showing the power spectrum density of target data U(k). FIG. 13 is a diagram showing an example of displacement data M _s (k). FIG. 13 is a diagram showing the power spectrum density of displacement data M _s (k). FIG. 13 is a diagram showing an example of vibration component data U _OSC (k). FIG. 13 is a diagram showing an example of displacement data MU(k). FIG. 13 is a diagram showing an example of first section correction data M _CC1 (k) and third section correction data M _CC3 (k). FIG. 13 is a diagram showing an example of second section first correction data M1 _CC2 (k). FIG. 4 is a diagram showing an example of a straight line L _C (k). FIG. 13 is a diagram showing an example of second section second correction data M2 _CC2 (k). FIG. 13 is a diagram showing an example of second section correction data M _CC2 (k). FIG. 13 is a diagram showing an example of correction data M _CC (k). FIG. 13 is a diagram showing an example of displacement data RU(k). FIG. 4 is a diagram showing an example of measurement data U'(k). FIG. 4 is a diagram showing an example of a displacement waveform UO(k) and drift noise D(k). FIG. 13 is a diagram showing an example of target data U(k). FIG. 13 is a diagram showing measurement data U'(k). FIG. 13 is a diagram showing measurement data U'(k) and a displacement waveform UO(k) superimposed on each other. FIG. 4 is a flowchart showing an example of a procedure of a measurement method according to the first embodiment. FIG. 4 is a flowchart showing an example of a procedure of a correction data estimation process in the first embodiment. 1A and 1B are diagrams showing configuration examples of a sensor, a measuring device, and a monitoring device. FIG. 11 is a flowchart showing an example of a procedure of a measurement method according to a second embodiment. FIG. 11 is a flowchart showing an example of a procedure of a correction data estimation step in the second embodiment. FIG. 13 is a diagram showing an example of the arrangement of a measurement apparatus according to a second embodiment. FIG. 13 is a diagram showing another example of the configuration of the measurement system. FIG. 13 is a diagram showing another example of the configuration of the measurement system. FIG. 13 is a diagram showing another example of the configuration of the measurement system. A cross-sectional view of the upper structure of Figure 39 taken along line AA.

The following describes in detail preferred embodiments of the present invention with reference to the drawings. Note that the embodiments described below do not unduly limit the content of the present invention described in the claims. Furthermore, not all of the configurations described below are necessarily essential components of the present invention.

1. First embodiment 1-1. Configuration of a measurement system In the following, a measurement system for implementing the measurement method of the present embodiment will be described using an example in which the structure is the superstructure of a bridge and the moving body is a railway vehicle.

Figure 1 is a diagram showing an example of a measurement system according to this embodiment. As shown in Figure 1, the measurement system 10 according to this embodiment includes a measurement device 1 and at least one sensor 2 provided on the superstructure 7 of a bridge 5. The measurement system 10 may also include a monitoring device 3.

The bridge 5 is made up of a superstructure 7 and a substructure 8. Figure 2 is a cross-sectional view of the superstructure 7 taken along line A-A in Figure 1. As shown in Figures 1 and 2, the superstructure 7 includes a bridge deck 7a consisting of a deck F, main girders G, and cross beams (not shown), supports 7b, rails 7c, sleepers 7d, and ballast 7e. As shown in Figure 1, the substructure 8 includes a pier 8a and an abutment 8b. The superstructure 7 is a structure that spans either one of the adjacent abutments 8b and pier 8a, two adjacent abutments 8b, or two adjacent piers 8a. Both ends of the superstructure 7 are located at the positions of the adjacent abutments 8b and pier 8a, the positions of the two adjacent abutments 8b, or the positions of the two adjacent piers 8a.

The measuring device 1 and each sensor 2 are connected, for example, by a cable (not shown), and communicate with each other via a communication network such as a CAN. CAN stands for Controller Area Network. Alternatively, the measuring device 1 and each sensor 2 may communicate with each other via a wireless network.

For example, each sensor 2 outputs data for calculating the displacement of the superstructure 7 due to the movement of the railway vehicle 6, which is a moving body. In this embodiment, each sensor 2 is an acceleration sensor, and may be, for example, a quartz acceleration sensor or a MEMS acceleration sensor. MEMS is an abbreviation for Micro Electro Mechanical Systems.

In this embodiment, each sensor 2 is installed in the longitudinal center of the superstructure 7, specifically, in the longitudinal center of the main girder G. However, as long as each sensor 2 can detect the acceleration for calculating the displacement of the superstructure 7, its installation position is not limited to the central part of the superstructure 7. If each sensor 2 is installed on the deck F of the superstructure 7, there is a risk that it will be destroyed by the running of the railway vehicle 6, and there is also a risk that the measurement accuracy will be affected by local deformation of the bridge deck 7a. Therefore, in the example of Figures 1 and 2, each sensor 2 is installed on the main girder G of the superstructure 7.

The floor plates F and main girders G of the superstructure 7 are deflected vertically due to the load of the railway vehicle 6 traveling on the superstructure 7. Each sensor 2 detects the acceleration of the deflection of the floor plates F and main girders G due to the load of the railway vehicle 6 traveling on the superstructure 7.

The measuring device 1 calculates the deflection displacement of the superstructure 7 caused by the running of the railway vehicle 6 based on the acceleration data output from each sensor 2. The measuring device 1 is installed, for example, on the bridge abutment 8b.

The measuring device 1 and the monitoring device 3 can communicate with each other via a communication network 4, such as a wireless network for mobile phones and the Internet. The measuring device 1 transmits information on the displacement of the superstructure 7 caused by the running of the railcar 6 to the monitoring device 3. The monitoring device 3 stores the information in a storage device (not shown) and may perform processing such as monitoring the railcar 6 and determining abnormalities in the superstructure 7 based on the information.

In this embodiment, the bridge 5 is a railway bridge, such as a steel bridge, a girder bridge, or an RC bridge. RC stands for Reinforced Concrete.

As shown in FIG. 2, in this embodiment, an observation point R is set in correspondence with the sensor 2. In the example of FIG. 2, the observation point R is set at a position on the surface of the superstructure 7 vertically above the sensor 2 installed on the main girder G. In other words, the sensor 2 is an observation device that observes the observation point R. The sensor 2 that observes the observation point R may be provided at a position where it can detect the acceleration generated at the observation point R due to the movement of the railway vehicle 6, but it is preferable that it be provided at a position close to the observation point R.

The number and installation positions of the sensors 2 are not limited to the examples shown in Figures 1 and 2, and various modifications are possible.

The measuring device 1 acquires acceleration in a direction intersecting the plane of the superstructure 7 along which the railcar 6 moves, based on acceleration data output from the sensor 2. The plane of the superstructure 7 along which the railcar 6 moves is defined by the direction along which the railcar 6 moves, i.e., the X direction, which is the longitudinal direction of the superstructure 7, and the Y direction, which is the width direction of the superstructure 7, which is perpendicular to the direction along which the railcar 6 moves. As the railcar 6 travels, the observation point R deflects in a direction perpendicular to the X and Y directions. Therefore, in order to accurately calculate the magnitude of the acceleration of the deflection, it is desirable for the measuring device 1 to acquire acceleration in a direction perpendicular to the X and Y directions, i.e., the Z direction which is the normal direction of the floor panel F.

Figure 3 is a diagram explaining the acceleration detected by sensor 2. Sensor 2 is an acceleration sensor that detects acceleration occurring in each of three mutually orthogonal axial directions.

To detect the acceleration of the deflection at observation point R caused by the movement of the railway vehicle 6, the sensor 2 is installed so that one of the three detection axes, the x-axis, y-axis, and z-axis, intersects with the X-direction and the Y-direction. In Figures 1 and 2, the sensor 2 is installed so that one axis intersects with the X-direction and the Y-direction. Since the observation point R deflects in a direction perpendicular to the X-direction and the Y-direction, in order to accurately detect the acceleration of the deflection, ideally the sensor 2 is installed so that one axis is aligned with the Z-direction perpendicular to the X-direction and the Y-direction, i.e., the normal direction of the floor board F.

However, when installing sensor 2 on superstructure 7, the installation location may be tilted. Even if one of the three detection axes of sensor 2 is not installed in the normal direction of floor board F, the error is small and negligible as long as it is roughly oriented in the normal direction. Furthermore, even if one of the three detection axes of sensor 2 is not installed in the normal direction of floor board F, measurement device 1 can correct detection errors due to the tilt of sensor 2 by using a three-axis composite acceleration combining accelerations of the x-axis, y-axis, and z-axis. Furthermore, sensor 2 may be a one-axis acceleration sensor that detects acceleration occurring in a direction approximately parallel to the vertical direction, or acceleration in the normal direction of floor board F.

Below, we will first explain the basic concept of the measurement method of this embodiment executed by the measurement device 1, and then explain the details.

1-2. Basic Concept of the Measurement Method First, the target data to be processed, which is obtained based on the acceleration data output from the sensor 2, is denoted as _Md (k), and as shown in formula (1), the target data _Md (k) contains a significant signal M(k) containing a vibration component and drift noise e(k). If the number of samples contained in the target data _Md (k) is denoted as N, k is an integer between 0 and N-1.

The drift noise e(k) is not primarily a signal input to the sensor 2, but an error signal generated inside the sensor 2 due to a zero point error, a drift due to temperature change, a drift due to nonlinear sensitivity, etc. The drift noise e(k) is a long-period fluctuation compared to the signal input to the sensor 2, and its energy is distributed in the low frequency range. Fig. 4 shows the relationship between the frequency characteristic F{ _Md (k)} of the target data _Md (k), the frequency characteristic F{M(k)} of the signal M(k), and the frequency characteristic F{e(k)} of the drift noise e(k).

The vibration components contained in the signal M(k) are, for example, a fundamental frequency signal component and its harmonic components generated by the natural vibration of the bridge 5, and generally have energy distributed in a higher frequency range than the drift noise e(k). Therefore, by low-pass filtering the target data M _d (k) as shown in equation (2), data M _s (k) in which the vibration components have been reduced can be obtained.

The low-pass filter process for reducing the vibration component may be a process of moving average the target data _Md (k) at a period corresponding to the fundamental frequency calculated based on the frequency characteristic F{ _Md (k)}, or may be an FIR filter process that attenuates signal components with frequencies equal to or higher than the fundamental frequency. FIR stands for Finite Impulse Response. Figure 5 shows the relationship between the frequency characteristic F{ _Ms (k)} of data _Ms (k) obtained by moving average processing of the target data _Md (k), the frequency characteristic F{M(k)} of the signal M(k), and the frequency characteristic F{e(k)} of the drift noise e(k).

Moreover, as shown in equation (3), data _Mv (k) including a vibration component can be obtained by subtracting data _Ms (k) from target data Md(k). Fig. 6 shows the frequency characteristic F{ _MV (k)} of data _Mv (k) including a vibration component.

If the data obtained by high-pass filtering data _Ms (k) is denoted as _fHP ( _Ms (k)) and the data obtained by low-pass filtering data _Ms (k) is denoted as _fLP ( _Ms (k)), the relationship between data _Ms (k), data _fHP ( _Ms (k)), and data _fLP ( _Ms (k)) is expressed by equation (4).

The relationship between the frequency characteristic F{ _Ms (k)} of data _Ms (k), the frequency characteristic F{ _fHP ( _Ms (k))} of data _fHP ( _Ms (k)), and the frequency characteristic F{ _fLP ( _Ms (k))} of data _fLP ( _Ms (k)) is expressed by equation (5). Fig. 7 shows the relationship between the frequency characteristics F{ _Ms (k)}, F{ _fHP ( _Ms (k))}, and F{ _fLP ( _Ms (k))}.

Since the drift noise e(k) is observed as an offset error, high-pass filter processing that attenuates signals in the low frequency range is effective in removing the drift noise e(k). When data _Ms (k) is subjected to high-pass filter processing, the drift noise e(k) whose energy is distributed in the low frequency range is sufficiently suppressed, and it is assumed that the data _fHP ( _Ms (k)) after high-pass filter processing is approximately equal to the data _fHP (M(k)) obtained by high-pass filter processing of the signal M(k), as shown in equation (6).

Since the low-frequency signal components of the signal M(k) are also lost due to the high-pass filter processing, in order to compensate for these signal components, data f _LP (M(k)) obtained by low-pass filtering the signal M(k) is estimated from data f _HP ( _{M s} (k)) obtained by high-pass filtering the data M _s (k). As shown in equation (7), it is assumed that data f _LP (M(k)) obtained by low-pass filtering the signal M(k) is approximately equal to data A _LP (f _HP (M _s (k))) obtained by estimating data f _LP (M(k)) obtained by low-pass filtering the signal M(k) from data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k).

Assuming that data obtained by removing drift noise e(k) from data _Ms (k) as in equation (8) is equal to the sum of data _fHP ( _Ms (k)) obtained by high-pass filtering data _Ms (k) and data _fLP (M(k)) obtained by low-pass filtering signal M(k), equation (9) can be obtained from equations (6), (7), and (8).

From equation (9), the relationship between the frequency characteristic F{ _Ms '(k)} of data _Ms '(k), the frequency characteristic F{ _fHP ( _Ms (k))} of data _fHP ( _Ms (k)), and the frequency characteristic F{ _ALP ( _fHP ( _Ms (k)))} of data _ALP ( _fHP ( _Ms (k))) is expressed by equation (10). Fig. 8 shows the relationship between the frequency characteristics F{ _Ms '(k)}, F{ _fHP ( _Ms (k))}, and F{ _ALP ( _fHP ( _Ms (k)))}.

As shown in equation (11), data _Md '(k) approximating signal M(s) is obtained by adding data Ms _' (k) obtained by equation (9) and data _Mv (k) including vibration components. Figure 9 shows the relationship between frequency characteristics F{ _Md '(k)}, F{ _Ms '(k)}, and F{ _Mv (k)}.

By high-pass filtering the data _Ms (k), data _fHP ( _Ms (k)) in which the drift noise e(k) has been reduced can be obtained. From this data _fHP ( _Ms (k)), data _fLP (M(k)) obtained by low-pass filtering the signal M(k) can be estimated. The signal M(k) in which the drift noise e(k) has been reduced can be obtained by adding the data _fHP ( _Ms (k)), the estimated data, and the data _Mv (k) containing the vibration component.

In the following, a procedure for estimating data f _LP (M(k)) obtained by low-pass filtering a signal _M (k) from data f _HP (M _s (k)) obtained by high-pass filtering the data M _s (k) will be described using an example in which the data M s (k) is displacement data.

First, as shown in equation (12), a unit pulse waveform is assumed as data _Ms (k) that simplifies the deflection displacement of the superstructure 7 of the bridge 5 when a railway vehicle 6 passes through. In equation (12), k is an integer equal to or greater than 0. Fig. 10 shows data _Ms (k), which is a unit pulse waveform expressed by equation (12).

It is assumed that the relationship between data M _s (k), data f _HP (M _s (k)) obtained by high-pass filtering data M _s (k), and data f _LP (M _s (k)) obtained by low-pass filtering data M s (k) is as shown in Equation (13).

For example, if the low-pass filter process is a moving average process, equation (14) can be obtained from equation (13). At this time, data k is located in the center of the moving average interval 2p+1.

In equation (14), p is an integer equal to or greater than 1, and since we want to provide a flat portion in data f _LP (M _s (k)) obtained by low-pass filtering data M _s (k), we set p<(k _a -k _b )/2. Fig. 11 shows data f _LP (M _s (k)) obtained by low-pass filtering data M _s (k), which is a unit pulse waveform expressed by equation (12), using a moving average. Fig. 12 shows data f _HP (M _s (k)) obtained by high-pass filtering data M _s (k), which is a unit pulse waveform expressed by equation (12).

11 and 12, data f _HP (M s (k)) obtained by subjecting unit pulse waveform data M _s (k) to high-pass filtering will be compared with data f _LP (M _s (k)) obtained by subjecting unit pulse waveform data M _s (k) to low-pass filtering.

As shown in FIG. 11, the slope b of the section from k _a −p to k _a +p of data f _LP (M _s (k)) obtained by low-pass filtering data M _s (k) is calculated by equation (15).

Furthermore, the slope of the section of data f _LP (M _s (k)) from k _b -p to k _b +p is -b, and the amplitude B of the section from k _a +p to k _b -p is -1.

On the other hand, as shown in FIG. 12, the slope a of the section from k _a -p to k _a of data f _HP (M _s (k)) obtained by high-pass filtering data M _s (k) is calculated by equation (16).

Furthermore, the slope of the section of data f _HP (M _s (k)) from k _b to k _b +p is −a, and the amplitude A at k=k _a −1 is calculated by equation (17).

By substituting the above equation (12) into equation (17), the amplitude A is calculated as shown in equation (18).

From equation (18), if p is sufficiently large, the amplitude A becomes 1/2.

Here, the unit pulse waveform shown in equation (12) assumed as data _Ms (k) does not include drift noise e(k). Therefore, data fLP( _Ms (k)) obtained by low-pass filtering data Ms(k) is equal to data _fLP ( _M (k)) obtained by low-pass filtering signal _M (k). Therefore, the comparison between data _fHP ( _Ms (k)) and data _fLP ( _Ms (k)) is a comparison between data _fHP ( _Ms (k)) and data _fLP (M(k)), and by measuring the slope a and amplitude A of data _fHP ( _Ms (k)), data _fLP (M(k)) obtained by low-pass filtering signal M(k) can be estimated.

1-3. Details of the measurement method In reality, the target data U(k), which is displacement data of the deflection of the superstructure 7 of the bridge 5 when the railway vehicle 6 passes, includes data of a waveform that is convex in the positive or negative direction different from the unit pulse waveform, but by replacing the target data M _d (k) with the target data U(k) and estimating data f _LP (M(k)) obtained by low-pass filtering the signal M(k) based on the estimation method described above, it is possible to estimate. For example, the waveform that is convex in the positive or negative direction is a rectangular waveform, a trapezoidal waveform, or a half-sine waveform.

First, the measurement device 1 integrates the acceleration data A _s (k) output from the acceleration sensor to generate velocity data V _s (k) as shown in equation (19), and then integrates the velocity data V _s (k) to generate target data U(k) as shown in equation (20). In equations (19) and (20), ΔT is the time interval between data. An example of the target data U(k) is shown in FIG. 13.

Next, the measurement device 1 generates displacement data M _s (k) by low-pass filtering the target data U(k) in order to reduce the vibration component of the fundamental frequency _Ff contained in the target data U(k) and its harmonics.

Specifically, the measurement device 1 first performs a fast Fourier transform process on the target data U(k) to calculate the power spectrum density, and calculates the peak of the power spectrum density as the fundamental frequency _Ff . FIG. 14 shows the power spectrum density obtained by performing a fast Fourier transform process on the target data U(k) of FIG. 13. In the example of FIG. 14, the fundamental frequency _Ff is calculated as about 3 Hz. Then, the measurement device 1 calculates the fundamental period _{Tf from the fundamental frequency Ff by} equation (21), and calculates the moving average section _kmf adjusted to the time resolution of the data by dividing the fundamental period _Tf by ΔT as shown in equation (22). The fundamental period _Tf is a period corresponding to the fundamental frequency _Ff , and _Tf >2ΔT.

Then, the measuring device 1 performs moving average processing of the target data U(k) with the fundamental period _Tf according to the formula (23) as a low-pass filter processing, and generates displacement data Ms(k) as vibration component reduced data in which the vibration components contained in the signal _M (k) are reduced. This moving average processing not only requires a small amount of calculation, but also has a very large attenuation of the signal component of the fundamental frequency _Ff and its harmonic components, so that displacement data _Ms (k) in which the vibration components are effectively reduced can be obtained. FIG. 15 shows an example of the displacement data _Ms (k). FIG. 16 shows the power spectrum density of the displacement data _Ms (k). As shown in FIG. 15 and FIG. 16, displacement data _Ms (k) in which most of the vibration components contained in the target data U(k) are removed can be obtained.

The measurement device 1 may generate the displacement data _Ms (k) by performing FIR filter processing on the target data U(k) as the low-pass filter processing, which attenuates signal components with frequencies equal to or greater than the fundamental period _Tf . FIR stands for Finite Impulse Response. This FIR filter processing requires a larger amount of calculation than moving average processing, but can attenuate all signal components with frequencies equal to or greater than the fundamental frequency _Ff .

Next, the measurement device 1 subtracts the displacement data _Ms (k) in which the vibration component has been reduced from the target data U(k) using equation (24) to generate vibration component data _UOSC (k) containing the vibration component. An example of the vibration component data _UOSC (k) is shown in FIG. 17.

Furthermore, the measurement device 1 generates displacement data MU(k) by subjecting the displacement data M _s (k) to high-pass filtering in order to reduce drift noise, as shown in equation (25). Fig. 18 shows an example of the displacement data MU(k).

Next, based on the displacement data MU(k), the measurement device 1 estimates data f _LP (M(k)) obtained by low-pass filtering the signal M(k), i.e., correction data M _CC (k) corresponding to the difference between the displacement data MU(k) and the data obtained by removing drift noise from the displacement data M _S (k).

As shown in Fig. 18, in this embodiment, the measurement device 1 identifies a first section T1, a second section T2, and a third section T3 based on the displacement data MU(k), and generates correction data _MCC (k) divided into these three sections. In order to identify the first section T1, the second section T2, and the third section T3, the measurement device 1 calculates a first peak _p1 = ( _k1 , _mu1 ) and a second peak _p2 = ( _k2 , _mu2 ) of the displacement data MU(k). As shown in Fig. 18, the first peak _p1 is the leading peak near the time when the railcar 6 enters the superstructure 7, and the second peak _p2 is the trailing peak near the time when the railcar 6 leaves the superstructure 7. The first section T1 is the section before the first peak _p1 , that is, the section where k ≤ _k1 . The second section T2 is a section between the first peak _p1 and the second peak _p2 , i.e., a section where _k1 < k < _k2 . The third section T3 is a section after the second peak _p2 , i.e., a section where k2 _≦ k.

As shown in equation (26), the correction data M _CC (k) is calculated as the sum of the first section correction data M _CC1 (k), which is the correction data for the first section T1, the second section correction data M _CC2 (k), which is the correction data for the second section T2, and the third section correction data M _CC3 (k), which is the correction data for the third section T3.

The first section correction data M _CC1 (k) is calculated by equation (27) using data MU'(k) obtained by inverting the sign of the displacement data MU(k). Similarly, the third section correction data M _CC3 (k) is calculated by equation (28) using data MU'(k) obtained by inverting the sign of the displacement data MU(k). An example of the first section correction data M _CC1 (k) and the third section correction data M _CC3 (k) is shown in FIG. 19.

The second section correction data M _CC2 (k) is obtained as follows. First, in the section k≦(k ₁ +k ₂ )/2 before a predetermined time in the second section T2, data obtained by rearranging the displacement data MU(k) before the first peak _p1 in reverse order to the first peak _p1 and after is MU(2k ₁ -k). Also, in the section (k ₁ +k ₂ )/2≦k after the predetermined time in the second section T2, data obtained by rearranging the displacement data MU( _k ) after the second peak p2 in reverse order to the second peak _p2 and before is MU(2k ₂ -k). Here, the predetermined time is a time corresponding to k=k ₁ +k ₂ , but it may be another time.

Then, the data MU(2k ₁ -k) and the data MU(2k ₂ -k) are used to find the second section first correction data M1 _CC2 (k) according to the formula (29). An example of the second section first correction data M1 _CC2 (k) is shown in FIG.

The straight line L _C (k) passing through the first peak p ₁ =(k ₁ , mu ₁ ) and the second peak p2 =(k ₂ , mu ₂ ) can be obtained by equation (30). Fig. 21 shows an example of the straight line L _C (k).

According to the formula (31), the second section second correction data M2 _CC2 (k) is obtained by multiplying the straight line L _C (k) by −2 to obtain straight line data −2L _C (k). An example of the second section second correction data M2 _CC2 (k) is shown in FIG. 22.

As shown in formula (32), the second section correction data M _CC2 (k) is calculated as the sum of the second section first correction data M1 _CC2 (k) and the second section second correction data M2 _CC2 (k). An example of the second section correction data M _CC2 (k) is shown in FIG. 23.

The correction data M _CC (k) can be obtained by substituting the formulas (27), (28), and (32) into the formula (26) as shown in the formula (33). An example of the correction data M _CC (k) is shown in FIG.

Then, as shown in equation (34), the displacement data MU(k) and the correction data M _CC (k) are added together to obtain the displacement data RU(k) in which the vibration component and drift noise have been reduced.

By substituting equation (33) into equation (34), we obtain equation (35).

Equation (35) is transformed into equation (36).

According to equation (36), the displacement data RU(k) is 0 in the first interval T1, where k≦ _k1 , and in the second interval T2, where _k2 ≦k, and the displacement data RU(k) from which vibration components and drift noise have been removed is obtained. Fig. 25 shows an example of the displacement data RU(k).

Then, as shown in equation (37), the displacement data RU(k) and the vibration component data U _OSC (k) are added to obtain measurement data U'(k), which is displacement data with reduced drift noise. An example of the measurement data U'(k) is shown in FIG. 26.

By substituting equation (36) into equation (37), we obtain equation (38).

To confirm the effect of removing drift noise by the measurement method of this embodiment, a waveform obtained by adding drift noise D(k) to the displacement waveform UO(k) as shown in equation (39) is used as the target data U(k). Figure 27 shows an example of the displacement waveform UO(k) and drift noise D(k). Figure 28 shows an example of the target data U(k).

The measurement data U'(k) and the displacement waveform UO(k) obtained by equations (21) to (38) for the target data U(k) are compared. Figure 29 shows the measurement data U'(k). Figure 30 shows the measurement data U'(k) and the displacement waveform UO(k) superimposed on each other. As shown in Figures 29 and 30, it can be confirmed that the measurement method of this embodiment can obtain measurement data U'(k) from which drift noise has been removed and the displacement waveform has been restored.

31 is a flow chart showing an example of the procedure of the measurement method of the first embodiment for measuring the displacement of the superstructure 7 of the bridge 5. In this embodiment, the measurement device 1 executes the procedure shown in FIG.

As shown in FIG. 31, first, in the target data generation step S1, the measurement device 1 acquires acceleration data A _s (k) which is observation data, and generates target data U(k). Therefore, the target data U(k) is data based on the acceleration data A _s (k) which is observation data by the sensor 2 which is an observation device. Specifically, the measurement device 1 performs calculations of the above-mentioned formula (19) and formula (20) to generate the target data U(k). In this embodiment, the target data U(k) which is the processing target is data of the displacement of the superstructure 7 by the railway vehicle 6 which is a moving body moving on the superstructure 7 which is a structure, and is data obtained by integrating twice the acceleration in the direction intersecting with the surface of the superstructure 7 on which the railway vehicle 6 moves. Therefore, the target data U(k) includes data of a waveform which is convex in the positive or negative direction, specifically, a rectangular waveform, a trapezoidal waveform, or a half-sine waveform. Note that the rectangular waveform includes not only an exact rectangular waveform but also a waveform which approximates a rectangular waveform. Similarly, a trapezoidal waveform includes not only exact trapezoidal waveforms but also waveforms that approximate trapezoidal waveforms. Similarly, a half-sine waveform includes not only exact half-sine waveforms but also waveforms that approximate half-sine waveforms.

Next, in the low-pass filter processing step S2, the measurement device 1 performs low-pass filter processing on the target data U(k) including the drift noise and vibration components generated in step S1 to generate displacement data _Ms (k) as vibration component reduced data in which the vibration components are reduced. For example, the measurement device 1 may perform fast Fourier transform processing on the target data U(k) to calculate the fundamental frequency _Ff , and as the low-pass filter processing, perform moving average processing on the target data U(k) with the fundamental period _Tf corresponding to the fundamental frequency _Ff as in the above-mentioned formula (23) to generate the displacement data _Ms (k). Also, for example, the measurement device 1 may perform fast Fourier transform processing on the target data U(k) to calculate the fundamental frequency _Ff , and as the low-pass filter processing, perform FIR filter processing on the target data U(k) to attenuate signal components with frequencies equal to _or higher than the fundamental frequency Ff to generate the displacement data _Ms (k).

Next, in the high-pass filter processing step S3, the measurement device 1 performs high-pass filter processing on the displacement data _Ms (k) including drift noise generated in step S2, as in the above-mentioned formula (25), to generate displacement data MU(k) as drift noise reduced data in which the drift noise is reduced. The high-pass filter processing of the displacement data _Ms (k) may be processing of subtracting data obtained by performing low-pass filter processing on the displacement data _Ms (k) from the displacement data _Ms (k), as in the above-mentioned formula (14). The low-pass filter processing may be moving average processing or FIR filter processing. In other words, the high-pass filter processing of the displacement data _Ms (k) may be processing of subtracting data obtained by performing moving average processing or FIR filter processing on the displacement data _Ms (k) from the displacement data _Ms (k).

Next, in a correction data estimation step S4, the measurement device 1 estimates correction data M CC (k) corresponding to the difference between the displacement data _MU (k) and data obtained by removing drift noise from the displacement data M S (k), based on the displacement data _MU (k) generated in step S3. Specifically, the measurement device 1 generates the correction data M _CC (k) by performing the calculations of the above-mentioned equations (26) to (33).

In addition, in the vibration component data generation step S5, the measurement device 1 subtracts the displacement data M s (k) generated in step S2 from the target data U (k) generated in step S1 to generate vibration component data _{U OSC} (k) including a vibration component, as shown in the above formula (24). In this embodiment, the frequency of the drift noise included in the target data U (k) is lower than the minimum value of the natural vibration frequency of the upper structure 7. The minimum value of the natural vibration frequency of the upper structure 7 is, for example, the frequency of the first vibration mode in the longitudinal direction of the upper structure 7. By setting the cutoff frequency of the low-pass filter processing in step S2 and the cutoff frequency of the high-pass filter processing in step S3 to be higher than the frequency of the drift noise of the upper structure 7 and lower than the minimum value of the natural vibration frequency, in the vibration component data U _OSC (k) generated in step S5, the drift noise is reduced without reducing the signal component of the natural vibration frequency of the upper structure 7 and its harmonic components. For example, the frequency of the drift noise may be less than 1 Hz, and the cutoff frequency of the low-pass filter process and the cutoff frequency of the high-pass filter process may be 1 Hz or higher.

Next, in the measurement data generation process S6, the measurement device 1 generates measurement data U'(k) by adding the displacement data MU(k) generated in process S3, the correction data M _CC (k) generated in process S4, and the vibration component data U _OSC (k) generated in process S5, as shown in the above-mentioned equations (34) and (37).

Next, in a measurement data output process S7, the measurement device 1 outputs the measurement data U'(k) generated in process S6 to the monitoring device 3. Specifically, the measurement device 1 transmits the measurement data U'(k) to the monitoring device 3 via the communication network 4.

Then, in step S8, the measuring device 1 repeats steps S1 to S7 until it has finished measuring the displacement of the superstructure 7 of the bridge 5.

Figure 32 is a flowchart showing an example of the procedure for the correction data estimation step S4 in Figure 31.

As shown in Fig. 32, first, in the section identification step S41, the measurement device 1 calculates the first peak _p1 = ( _k1 , _mu1 ) and the second peak _p2 = ( _k2 , _mu2 ) of the displacement data MU(k), and identifies a first section T1 before the first peak _p1 , a second section T2 between the first peak _p1 and the second peak _p2 , and a third section T3 after the second peak _p2 . That is, the first section T1 is a section where k ≦ _k1 , the second section T2 is a section where _k1 < k < _k2 , and the third section T3 is a section where _k2 ≦ k. In this embodiment, the first peak _p1 is the leading peak near the time when the railcar 6 enters the superstructure 7, and the second peak _p2 is the trailing peak near the time when the railcar 6 leaves the superstructure 7.

Next, in a first section correction data generating step S42, the measurement instrument 1 inverts the sign of the displacement data MU(k) in the first section T1 to generate first section correction data M _CC1 (k), as in the above-mentioned equation (27).

Next, in the second section correction data generation process S43, the measurement device 1 adds, before a predetermined time in the second section T2, data MU(2k ₁ -k) obtained by rearranging the displacement data MU(k) before the first peak _p1 in reverse order to after the first peak _p1 , and straight line data -2L C (k) obtained by multiplying a straight line L _C (k) passing through the first peak _p1 and the second peak _p2 by -2, as in the above-mentioned equation ₍ 32), and after the predetermined time in the second section T2, adds data MU(2k ₂ -k) obtained by rearranging the displacement data MU(k) after the _second peak p2 in reverse order to before the second peak _p2 , and the straight line data -2L _C (k) to generate the second section correction data M _CC2 (k).

Next, in a third interval correction data generating step S44, the measurement instrument 1 inverts the sign of the displacement data MU(k) in the third interval T3 to generate third interval correction data M _CC3 (k), as in the above-mentioned equation (28).

Finally, in the correction data generation process S45, the measurement device 1 generates correction data M CC (k) by adding the first section correction data M _CC1 (k) generated in process S42, the second section correction data M _CC2 (k) generated in process S43, and the third section correction data M _CC3 (k) generated in process _S44 , as shown in the above-mentioned equation (26).

1-5. Configurations of Observation Device, Measuring Device, and Monitoring Device FIG. 33 is a diagram showing an example of the configuration of the sensor 2, measuring device 1, and monitoring device 3, which are the observation devices.

As shown in FIG. 33, the sensor 2 includes a communication unit 21, an acceleration sensor 22, a processor 23, and a memory unit 24.

The storage unit 24 is a memory that stores various programs, data, etc., for the processor 23 to perform calculation processing and control processing. The storage unit 24 also stores programs, data, etc., for the processor 23 to realize a specified application function.

The acceleration sensor 22 detects the acceleration occurring in each of the three axial directions.

The processor 23 executes an observation program 241 stored in the memory unit 24 to control the acceleration sensor 22, generate observation data 242 based on the acceleration detected by the acceleration sensor 22, and store the generated observation data 242 in the memory unit 24. In this embodiment, the observation data 242 is acceleration data A _s (k).

Under the control of the processor 23, the communication unit 21 transmits the observation data 242 stored in the memory unit 24 to the measurement device 1.

As shown in FIG. 33, the measurement device 1 includes a first communication unit 11, a second communication unit 12, a processor 13, and a memory unit 14.

The first communication unit 11 receives the observation data 242 from the sensor 2, and outputs the received observation data 242 to the processor 13. As described above, the observation data 242 is acceleration data A _s (k).

The storage unit 14 is a memory that stores programs, data, etc. for the processor 13 to perform calculation processing and control processing. The storage unit 14 also stores various programs, data, etc. for the processor 13 to realize a specified application function. The processor 13 may also receive various programs, data, etc. via the communication network 4 and store them in the storage unit 14.

The processor 13 acquires the observation data 242 received by the first communication unit 11 and stores it in the memory unit 14 as observation data 142. The processor 13 then generates measurement data 143 based on the observation data 142 stored in the memory unit 14, and stores the generated measurement data 143 in the memory unit 14. In this embodiment, the measurement data 143 is measurement data U'(k).

In this embodiment, the processor 13 executes the measurement program 141 stored in the memory unit 14, thereby functioning as a target data generation unit 131, a low-pass filter processing unit 132, a high-pass filter processing unit 133, a correction data estimation unit 134, a vibration component data generation unit 135, a measurement data generation unit 136, and a measurement data output unit 137. That is, the processor 13 includes the target data generation unit 131, the low-pass filter processing unit 132, the high-pass filter processing unit 133, the correction data estimation unit 134, the vibration component data generation unit 135, the measurement data generation unit 136, and the measurement data output unit 137.

The target data generation unit 131 reads out the observation data 142 stored in the storage unit 14, and generates the target data U(k) based on the acceleration data A _s (k) which is the observation data 142. Specifically, the target data generation unit 131 generates the target data U(k) by performing the calculations of the above-mentioned equations (19) and (20). That is, the target data generation unit 131 performs the process of the target data generation step S1 in FIG.

The low-pass filter processing unit 132 performs low-pass filter processing on the target data U(k) including drift noise and vibration components generated by the target data generating unit 131 to generate displacement data M _s (k) as vibration component reduced data in which the vibration components have been reduced. That is, the low-pass filter processing unit 132 performs the processing of the low-pass filter processing step S2 in FIG. 31 .

The high-pass filter processing unit 133 performs high-pass filtering on the displacement data M _s (k) generated by the low-pass filter processing unit 132 to generate displacement data MU(k) as drift noise reduced data in which drift noise has been reduced, as shown in the above-mentioned equation (25). That is, the high-pass filter processing unit 133 performs the processing of the high-pass filter processing step S3 in FIG.

The correction data estimation unit 134 generates correction data M CC (k) corresponding to the difference between the displacement data MU(k) and data obtained by removing drift noise from the displacement data M _s (k), based on the displacement data _MU (k) generated by the high-pass filter processing unit 133. The correction data estimation unit 134 generates the correction data M _CC (k) by performing the calculations of the above-mentioned equations (26) to (33).

Specifically, first, the correction data estimation unit 134 calculates the first peak _p1 = ( _k1 , mu1) and the second peak _p2 = ( _k2 , _mu2 ) of the displacement data MU( _k ), and specifies a first section T1 before the first peak _p1 , a second section T2 between the first peak _p1 and the second peak _p2 , and a third section T3 after the second peak _p2 . That is, the correction data estimation unit 134 performs the process of the section specification step S41 in FIG.

Next, the correction data estimation unit 134 inverts the sign of the displacement data MU(k) in the first section T1 to generate first section correction data M _CC1 (k) as in the above-mentioned equation (27). That is, the correction data estimation unit 134 performs the process of the first section correction data generation step S42 in FIG.

Next, the correction data estimation unit 134 adds data MU(2k 1 -k) obtained by rearranging the displacement data MU(k) before the first peak _p1 in reverse order to the first peak _p1 and after, and straight line data -2L _C (k) obtained by multiplying a straight line L _C (k) passing through the first peak _p1 and the second peak _p2 by -2 before a predetermined time in the second section T2, and adds data MU(2k ₂ -k) obtained by rearranging the displacement data MU(k) after the second peak _p2 in reverse order to the second _{peak p2} and before, and straight line data -2L _C (k) after the predetermined time in the second section T2 to generate second section correction data M _CC2 (k). That is, the correction data estimation unit 134 performs the process of the second section correction data generation step S43 in FIG.

Next, the correction data estimation unit 134 inverts the sign of the displacement data MU(k) in the third section T3 to generate third section correction data M _CC3 (k) as in the above-mentioned equation (28). That is, the correction data estimation unit 134 performs the process of the third section correction data generation step S44 in FIG.

Finally, the correction data estimation unit 134 generates correction data M CC (k) by adding the first interval correction data M _CC1 (k), the second interval correction data M _CC2 (k), and the third interval correction data M _CC3 (k) as in the above-mentioned formula ( ₂₆ ). That is, the correction data estimation unit 134 performs the process of the correction data generation step S45 in FIG. 32.

In this way, the correction data estimation unit 134 performs the processing of the correction data estimation step S4 in FIG. 31, specifically, steps S41 to S45 in FIG. 32.

The vibration component data generating unit 135 generates vibration component data U OSC (k) including a vibration component by subtracting the displacement data M _s (k) generated by the low-pass filter processing unit 132 from the object data U(k) generated by the object data generating _{unit 131, as in the above-mentioned equation} (24). That is, the vibration component data generating unit 135 performs the process of the vibration component data generating step S5 in FIG.

The measurement data generating unit 136 generates measurement data U'(k) by adding the displacement data MU(k) generated by the high-pass filter processing unit 133, the correction data M _CC (k) generated by the correction data estimating unit 134, and the vibration component data U _OSC (k) generated by the vibration component data generating unit 135, as in the above-mentioned formulas (34) and (37). That is, the measurement data generating unit 136 performs the process of the measurement data generating step S6 in Fig. 31. The measurement data U' generated by the measurement data generating unit 136 is stored in the storage unit 14 as measurement data 143.

The measurement data output unit 137 reads out the measurement data 143 stored in the memory unit 14 and outputs the measurement data 143 to the monitoring device 3. Then, under the control of the measurement data output unit 137, the second communication unit 12 transmits the measurement data 143 stored in the memory unit 14 to the monitoring device 3 via the communication network 4. That is, the measurement data output unit 137 performs the process of the measurement data output step S7 in FIG. 31.

In this way, the measurement program 141 is a program that causes the measurement device 1, which is a computer, to execute each step of the flowchart shown in FIG. 31.

As shown in FIG. 33, the monitoring device 3 includes a communication unit 31, a processor 32, a display unit 33, an operation unit 34, and a memory unit 35.

The communication unit 31 receives the measurement data 143 from the measurement device 1 and outputs the received measurement data 143 to the processor 32. As described above, the measurement data 143 is the measurement data U'(k).

The display unit 33 displays various information under the control of the processor 32. The display unit 33 may be, for example, a liquid crystal display or an organic EL display. EL is an abbreviation for Electro Luminescence.

The operation unit 34 outputs operation data corresponding to an operation by a user to the processor 32. The operation unit 34 may be, for example, an input device such as a mouse, a keyboard, or a microphone.

The storage unit 35 is a memory that stores various programs, data, etc., for the processor 32 to perform calculation processing and control processing. The storage unit 35 also stores programs, data, etc., for the processor 32 to realize a specified application function.

The processor 32 acquires the measurement data 143 received by the communication unit 31, evaluates the change over time in the displacement of the superstructure 7 based on the acquired measurement data 143, generates evaluation information, and displays the generated evaluation information on the display unit 33.

In this embodiment, the processor 32 functions as a measurement data acquisition unit 321 and a monitoring unit 322 by executing a monitoring program 351 stored in the memory unit 35. That is, the processor 32 includes the measurement data acquisition unit 321 and the monitoring unit 322.

The measurement data acquisition unit 321 acquires the measurement data 143 received by the communication unit 31, and adds the acquired measurement data 143 to the measurement data string 352 stored in the memory unit 35.

The monitoring unit 322 statistically evaluates the change in displacement of the superstructure 7 over time based on the measurement data sequence 352 stored in the memory unit 35. The monitoring unit 322 then generates evaluation information indicating the evaluation results and displays the generated evaluation information on the display unit 33. The user can monitor the condition of the superstructure 7 based on the evaluation information displayed on the display unit 33.

The monitoring unit 322 may perform processing such as monitoring the railway vehicle 6 and determining abnormalities in the superstructure 7 based on the measurement data sequence 352 stored in the memory unit 35.

The processor 32 also transmits information for adjusting the operating conditions of the measuring device 1 and the sensor 2 to the measuring device 1 via the communication unit 31 based on the operation data output from the operation unit 34. The operating conditions of the measuring device 1 are adjusted based on the information received via the second communication unit 12. The measuring device 1 also transmits information for adjusting the operating conditions of the sensor 2 received via the second communication unit 12 to the sensor 2 via the first communication unit 11. The operating conditions of the sensor 2 are adjusted based on the information received via the communication unit 21.

The functions of the processors 13, 23, and 32 may be realized by individual hardware, or may be realized by integrated hardware. For example, the processors 13, 23, and 32 include hardware, and the hardware may include at least one of a circuit for processing digital signals and a circuit for processing analog signals. The processors 13, 23, and 32 may be a CPU, a GPU, or a DSP. CPU stands for Central Processing Unit, GPU stands for Graphics Processing Unit, and DSP stands for Digital Signal Processor. The processors 13, 23, and 32 may be configured as a custom IC such as an ASIC to realize the functions of the respective parts, or may be realized by a CPU and an ASIC. ASIC stands for Application Specific Integrated Circuit, and IC stands for Integrated Circuit.

The storage units 14, 24, and 35 are composed of, for example, various IC memories such as ROM, flash ROM, and RAM, hard disks, memory cards, and other recording media. ROM stands for Read Only Memory, RAM stands for Random Access Memory, and IC stands for Integrated Circuit. The storage units 14, 24, and 35 include non-volatile information storage devices that are devices or media that can be read by a computer, and various programs, data, and the like may be stored in the information storage devices. The information storage devices may be optical disks such as optical disks (DVDs and CDs), hard disk drives, or various types of memories such as card-type memories and ROMs.

Note that while only one sensor 2 is shown in FIG. 33, multiple sensors 2 may each generate observation data 242 and transmit it to the measuring device 1. In this case, the measuring device 1 receives multiple observation data 242 transmitted from the multiple sensors 2, generates multiple measurement data 143, and transmits it to the monitoring device 3. The monitoring device 3 also receives the multiple measurement data 143 transmitted from the measuring device 1, and monitors the states of the multiple superstructures 7 based on the received multiple measurement data 143.

1-6. Effects In the measurement method of the first embodiment described above, the measurement device 1 uses the target data U(k) to be processed to generate displacement data M _s (k) with reduced vibration components and vibration component data U _OSC (k) including vibration components, generates displacement data MU(k) with reduced drift noise from the displacement data M _s (k), and estimates correction data M _CC (k) based on the displacement data MU(k). Since the vibration components of the displacement data MU(k) are reduced, correction data M _{CC (k) estimated with high accuracy can be obtained. And, since the correction data M CC (} k) corresponds to the difference between the data obtained by removing drift noise from the displacement data M _s (k) and the displacement data MU(k), it contains significant signal components removed by the high-pass filter processing. Therefore, according to the measurement method of the first embodiment, the measurement device 1 can generate measurement data U'(k) in which drift noise is reduced with respect to the target data U(k) by adding the displacement data MU(k), the correction data M _CC (k), and the vibration component data U _OSC (k). Also, according to the measurement method of the first embodiment, the measurement device 1 uses the target data U(k) to be processed to generate the displacement data MU(k), the correction data M _CC (k), and the vibration component data U _OSC (k), and can generate measurement data U _' (k) in which drift noise is reduced without preparing information for reducing drift noise in advance by adding the displacement data _MU (k), the correction data M CC (k), and the vibration component data U OSC (k). Therefore, by using the measurement method of the first embodiment, it is possible to obtain measurement data U'(k) with high accuracy regardless of changes in the environment, and to reduce costs.

In particular, according to the measurement method of the first embodiment, the measurement device 1 can identify the first section T1, the second section T2 and the third section T3 based on the characteristics of the displacement data MU(k) in which drift noise and vibration components have been reduced for the target data U(k), and generate appropriate first section correction data M _CC1 (k), second section correction data M _CC2 (k) and third section correction data M _CC3 (k), thereby improving the estimation accuracy of the correction data M _CC (k) generated by adding these together.

According to the measurement method of the first embodiment, the measurement device 1 performs moving average processing on the target data U(k) with a period _Tf corresponding to the fundamental frequency _Ff , which not only reduces the required calculation amount but also greatly attenuates the signal component of the fundamental frequency _Ff and its harmonic components, thereby obtaining displacement data _Ms (k) in which the vibration components are effectively reduced, thereby eliminating the influence of the vibration components and improving the estimation accuracy of the correction data _MCC (k). Alternatively, the measurement device 1 performs FIR filter processing on the target data U(k) to attenuate signal components of frequencies equal to or higher than the fundamental frequency _Ff to generate the displacement data _Ms (k), which requires a larger calculation amount than the moving average processing, but can attenuate all signal components of frequencies equal to or higher than the fundamental frequency _Ff , thereby eliminating the influence of vibration components of frequencies equal to or higher than the fundamental frequency _Ff and improving the estimation accuracy of the correction data _MCC (k).

According to the measurement method of the first embodiment, the measurement device 1 performs high-pass filtering on the displacement data _Ms (k) by subtracting data obtained by performing moving average processing or FIR filtering on the displacement data _Ms (k) from the displacement data _Ms (k), thereby easily performing high-pass filtering. Furthermore, in the moving average processing or FIR filtering, the group delay of each signal component included in the displacement data _Ms (k) is constant, so that the correction data _MCC (k) can be estimated with high accuracy.

In addition, in the measurement method of the first embodiment, the target data U(k) to be processed is data on the displacement of the superstructure 7 of the bridge 5 caused by a railway vehicle 6 moving on the superstructure 7. Therefore, according to the measurement method of the first embodiment, the measurement device 1 generates measurement data U'(k) with reduced drift noise, which is displacement data of the superstructure 7 caused by the movement of the railway vehicle 6, and therefore the displacement of the superstructure 7 of the bridge 5 can be measured with high accuracy.

In addition, according to the measurement method of the first embodiment, the measurement device 1 generates the target data U(k) to be processed by integrating twice the acceleration in a direction intersecting the surface of the superstructure 7 detected by the sensor 2 installed on the superstructure 7, so that the displacement of the superstructure 7 can be measured with high accuracy.

In addition, in the measurement method of the first embodiment, since the frequency of the drift noise contained in the target data U(k) is lower than the minimum value of the natural vibration frequency of the upper structure 7, the cutoff frequency of the low-pass filter processing and high-pass filter processing for the target data U(k) can be set higher than the frequency of the drift noise of the upper structure 7 and lower than the minimum value of the natural vibration frequency. Therefore, according to the measurement method of the first embodiment, it is possible to reduce the drift noise in the generated measurement data U'(k) without reducing the signal component of the natural vibration frequency of the upper structure 7 and its harmonic components.

Furthermore, in the measurement method of the first embodiment, since the target data U(k) to be processed contains data of a waveform that is convex in the positive or negative direction, for example, a rectangular waveform, a trapezoidal waveform, or a half-sine waveform, the measurement device 1 can generate more appropriate correction data M _CC (k) based on the characteristics of these waveforms, thereby improving the estimation accuracy of the generated correction data M _CC (k).

2. Second Embodiment In the second embodiment, the same components as those in the first embodiment are denoted by the same reference numerals, and descriptions that overlap with those in the first embodiment will be omitted or simplified. The following description will focus mainly on the differences from the first embodiment.

In the measurement method of the first embodiment, the measurement device 1 adds the first section correction data M _CC1 (k), the second section correction data M _CC2 (k), and the third section correction data M _CC3 (k) to generate the correction data M _CC (k), and adds the displacement data MU (k), the correction data M _CC (k), and the vibration component data U _OSC (k) to generate the measurement data U' (k). On the other hand, as in the above-mentioned formula (38), the measurement data U' (k) obtained by adding the displacement data MU (k), the correction data M _CC (k), and the vibration component data U _OSC (k) always becomes the vibration component data U _OSC (k) in the first section T1 and the third section T3. Therefore, in the measurement method of the second embodiment, the measurement device 1 does not generate the first section correction data M _CC1 (k) and the third section correction data M _CC3 (k), but generates correction data M _CC2 (k) in the second section T2. Then, as shown in formula (40), the measurement device 1 generates vibration component data U _OSC (k) in the first section T1, k _{≦ k1} , and the third section T3, k2 _≦ k, and adds the displacement data MU(k), the correction data M _CC2 (k), and the vibration component data U _OSC (k) in the _second section T2, k1 <k<k2, to generate measurement data U'(k).

In the above equation (38), since the correction data M _CC (k) matches the second interval correction data M _CC2 (k) in the interval k ₁ <k<k ₂ , the calculation result using equation (40) matches the calculation result using equation (38).

Figure 34 is a flow chart showing an example of the procedure of the measurement method of the second embodiment for measuring the displacement of the superstructure 7 of the bridge 5. In this embodiment, the measurement device 1 executes the procedure shown in Figure 34.

As shown in Fig. 34, first, in a target data generation step S110, the measurement device 1 acquires acceleration data A _s (k), which is observation data, and generates target data U(k). Specifically, the measurement device 1 performs calculations of the above-mentioned equations (19) and (20) to generate the target data U(k). The process of the target data generation step S110 is the same as the process of the target data generation step S1 in Fig. 31.

Next, in a low-pass filter processing step S120, the measurement device 1 performs low-pass filter processing on the target data U(k) containing the drift noise and vibration components generated in step S110 to generate displacement data M _s (k) as vibration component reduced data in which the vibration components have been reduced. The processing in the low-pass filter processing step S120 is the same as the processing in the low-pass filter processing step S2 in FIG. 31.

Next, in a high-pass filter processing step S130, the measurement device 1 performs high-pass filter processing on the displacement data M _s (k) including drift noise generated in step S120 to generate displacement data MU(k) as drift-noise reduced data in which the drift noise has been reduced, as shown in the above-mentioned equation (25). The processing in the high-pass filter processing step S130 is the same as the processing in the high-pass filter processing step S3 in FIG.

Next, in a section identification step S140, the measurement device 1 calculates a first peak _p1 = ( _k1 , mu1) and a second peak _p2 = ( _k2 , _mu2 ) of the displacement data MU(k) generated in step _S130 , and identifies a first section T1 before the first peak _p1 , a second section T2 between the first peak _p1 and the second peak _p2 , and a third section T3 after the second peak _p2 . That is, the first section T1 is a section where k≦ _k1 , the second section T2 is a section where _k1 <k< _k2 , and the third section T3 is a section where k2 _≦ k. The process of the section identification step S140 is the same as the process of the section identification step S41 in FIG. 32.

Next, in a correction data estimation step S150, the measurement device 1 generates correction data M CC2 (k) corresponding to the difference between the displacement data MU(k) and data obtained by removing drift noise from the displacement data M _s (k) in the second section T2, based on the displacement data MU(k) generated in step S130. Specifically, the measurement device 1 generates the correction data M _CC2 (k) by performing the calculations of the above-mentioned equations ( ₂₉ ) to (32).

Furthermore, in a vibration component data generating step S160, the measurement device 1 generates vibration component data U OSC (k) including a vibration component by subtracting the displacement data M _s (k) generated in step S120 from the target data _U (k) generated in step S110, as in the above-mentioned formula (24). The processing of the vibration component data generating step S160 is the same as the processing of the vibration component data generating step S5 in FIG.

Next, in the measurement data generation process S170, the measurement device 1 treats the first section T1 as the vibration component data U _OSC (k) generated in process S160, adds the displacement data MU(k) generated in process S130, the correction data M _CC2 (k) generated in process S150, and the vibration component data U _OSC (k) in the second section T2, and treats the third section T3 as the vibration component data U _OSC (k) to generate measurement data U'(k).

Next, in a measurement data output step S180, the measurement device 1 outputs the measurement data U'(k) generated in step S170 to the monitoring device 3. Specifically, the measurement device 1 transmits the measurement data U'(k) to the monitoring device 3 via the communication network 4. The process of the measurement data output step S180 is the same as the process of the measurement data output step S7 in FIG. 31.

Then, in step S190, the measuring device 1 repeats the processes of steps S110 to S180 until the measurement of the displacement of the superstructure 7 of the bridge 5 is completed.

Figure 35 is a flow chart showing an example of the procedure for the correction data estimation process S150 in Figure 34.

As shown in FIG. 35, in step S151, the measuring device 1 adds, before a predetermined time in the second section T2, data MU(2k ₁ -k) obtained by rearranging the displacement data MU(k) before the first peak _p1 in reverse order to after the first peak _p1 , and straight-line data -2L C (k) obtained by multiplying a straight line L _C (k) passing through the first peak _p1 and the second peak _p2 by _-2 , as in the above-mentioned equation (32), and after the predetermined time in the second section T2, adds data MU(2k ₂ -k) obtained by rearranging the displacement data MU(k) after the _second peak _p2 in reverse order to before the second peak p2, and the straight-line data -2L _C (k) to generate correction data M _CC2 (k).

Figure 36 is a diagram showing an example of the configuration of the measurement device 1 in the second embodiment. As shown in Figure 36, the measurement device 1 in the second embodiment includes a first communication unit 11, a second communication unit 12, a processor 13, and a memory unit 14, similar to the first embodiment. The functions of the first communication unit 11, the second communication unit 12, and the memory unit 14 are similar to those in the first embodiment, and therefore description thereof will be omitted.

In this embodiment, the processor 13 executes the measurement program 141 stored in the memory unit 14 to function as a target data generation unit 131, a low-pass filter processing unit 132, a high-pass filter processing unit 133, a correction data estimation unit 134, a vibration component data generation unit 135, a measurement data generation unit 136, a measurement data output unit 137, and a section identification unit 138. That is, the processor 13 includes the target data generation unit 131, the low-pass filter processing unit 132, the high-pass filter processing unit 133, a correction data estimation unit 134, a vibration component data generation unit 135, a measurement data generation unit 136, a measurement data output unit 137, and a section identification unit 138.

The functions of the target data generation unit 131, the low-pass filter processing unit 132, the high-pass filter processing unit 133, the vibration component data generation unit 135, and the measurement data output unit 137 are the same as those in the first embodiment, so their description will be omitted. The target data generation unit 131 performs the process of the target data generation step S110 in FIG. 34. The low-pass filter processing unit 132 performs the process of the low-pass filter processing step S120 in FIG. 34. The high-pass filter processing unit 133 performs the process of the high-pass filter processing step S130 in FIG. 34. The vibration component data generation unit 135 performs the process of the vibration component data generation step S160 in FIG. 34. The measurement data output unit 137 performs the process of the measurement data output step S180 in FIG. 34.

The section determination unit 138 calculates the first peak _p1 = ( _k1 , mu1) and the second peak _p2 = ( _k2 , _mu2 ) of the displacement data MU( _k ) generated by the high-pass filter processing unit 133, and determines a first section T1 before the first peak _p1 , a second section T2 between the first peak _p1 and the second peak _p2 , and a third section T3 after the second peak _p2 . That is, the section determination unit 138 performs the process of the section determination step S140 in FIG.

The correction data estimation unit 134 generates correction data M CC2 (k) corresponding to the difference between the displacement data MU(k) and data obtained by removing drift noise from the displacement data M _s (k) in the second section _T2 , based on the displacement data MU(k) generated by the high-pass filter processing unit 133. The correction data estimation unit 134 generates the correction data M _CC2 (k) by performing the calculations of the above-mentioned equations (29) to (32). Specifically, as in the above-mentioned equation (32), the correction data estimation unit 134 adds, before a predetermined time in the second section T2, data MU(2k ₁ -k) obtained by rearranging the displacement data MU(k) before the _first peak _p1 in reverse order to after the first peak p1, and straight-line data -2L _C (k) obtained by multiplying a straight line L _C (k) passing through the first peak _p1 and the second peak _p2 by _-2 , and after a predetermined time in the second section T2, adds data MU(2k ₂ -k) obtained by rearranging the displacement data MU(k) after the second peak _p2 in reverse order to before the second peak p2, and the straight-line data -2L _C (k) to generate the correction data M _CC2 (k).

In this way, the correction data estimation unit 134 performs the processing of the correction data estimation step S150 in FIG. 34, specifically, the processing of step S151 in FIG. 35.

The measurement data generating unit 136 generates measurement data U'(k) by treating the first section T1 as the vibration component data _UOSC (k) generated by the vibration component data generating unit 135, adding the displacement data MU(k) generated by the high-pass filter processing unit 133, the correction data _MCC2 (k) generated by the correction data estimating unit 134, and the vibration component data _UOSC (k) in the second section T2, and treating the third section T3 as the vibration component data _UOSC (k), as in the above-mentioned formula (40). That is, the measurement data generating unit 136 performs the process of the measurement data generating step S170 in FIG. 34. The measurement data U'(k) generated by the measurement data generating unit 136 is stored in the storage unit 14 as the measurement data 143.

In this way, the measurement program 141 is a program that causes the measurement device 1, which is a computer, to execute each step of the flowchart shown in FIG. 34.

In the measurement method of the second embodiment described above, the measurement device 1 generates displacement data Ms(k) in which the vibration component is reduced and vibration component data _UOSC (k) including the vibration component using the target data _U (k) to be processed. Then, the measurement device 1 generates displacement data MU(k) in which drift noise is reduced from the displacement data _Ms (k), identifies the first section T1, the second section T2, and the third section T3 based on the characteristics of the displacement data MU(k), and estimates the correction data _MCC (k) in the second section T2. The correction data _MCC (k) corresponds to the difference between the data obtained by removing the drift noise from the displacement data _Ms (k) and the displacement data MU(k) in the second section T2, and therefore contains significant signal components removed by the high-pass filter processing. Therefore, according to the measurement method of the second embodiment, the measurement device 1 uses the first section T1 and the third section T3 as vibration component data U _OSC (k), and adds the displacement data MU (k), the correction data M _CC (k), and the vibration component data U _OSC (k) in the second section T2, thereby generating measurement data U' (k) in which drift noise has been reduced with respect to the target data U (k). Also, according to the measurement method of the second embodiment, the measurement device 1 uses the target data U (k) to be processed to generate the displacement data MU (k), the correction data M _CC2 (k), and the vibration component data U _OSC (k), and adds the displacement data MU (k), the correction data M _CC2 (k), and the vibration component data U _OSC (k) in the second section T2, thereby generating measurement data U' (k) in which drift noise has been reduced without preparing information for reducing drift noise in advance. Therefore, by using the measurement method of the second embodiment, it is possible to obtain accurate measurement data U'(k) regardless of changes in the environment, and to reduce costs.

Furthermore, according to the measurement method of the second embodiment, the measurement device 1 does not need to generate correction data M CC1 (k) and M _CC3 (k) or add the displacement data MU(k), correction data M _CC1 (k) and M _CC3 (k), and vibration component data U _OSC (k) in the first section _T1 and the third section T3 to generate the measurement data U'(k), thereby reducing the amount of calculations.

In particular, according to the measurement method of the second embodiment, the measurement device 1 can generate more appropriate correction data M _CC2 (k) in the second section T2 based on the characteristics of the displacement data MU(k) in which drift noise and vibration components have been reduced for the target data U(k), thereby improving the estimation accuracy of the generated correction data M _CC2 (k).

In addition, the measurement method of the second embodiment can achieve the same effects as the measurement method of the first embodiment.

3. Modifications The present invention is not limited to the present embodiment, and various modifications are possible within the scope of the present invention.

In each of the above embodiments, the observation device is a sensor 2 that outputs acceleration data A _s (k), and the target data is target data U(k) obtained by integrating the acceleration data A _s (k) twice, but the observation device and the target data are not limited to this. For example, the observation device may be a contact type displacement meter, a ring type displacement meter, a laser displacement meter, a pressure sensor, a displacement measuring device using image processing, or a displacement measuring device using optical fibers, and the target data may be observation data of any of these observation devices. The contact type displacement meter, the ring type displacement meter, the laser displacement meter, the displacement measuring device using image processing, and the displacement measuring device using optical fibers measure the displacement of the observation point R due to the running of the railway vehicle 6. The pressure sensor detects the stress change of the observation point R due to the running of the railway vehicle 6. Also, for example, the observation device may be a speed sensor, and the target data may be data obtained by integrating the speed detected by the speed sensor. According to these measurement methods, the measurement device 1 can accurately measure the displacement of the superstructure 7 using data on the displacement, stress change, or speed.

As an example, FIG. 37 shows a configuration example of a measurement system 10 using a ring-type displacement meter as an observation device. FIG. 38 shows a configuration example of a measurement system 10 using a displacement measuring device by image processing as an observation device. In FIG. 37 and FIG. 38, the same components as in FIG. 1 are given the same reference numerals, and their description will be omitted. In the measurement system 10 shown in FIG. 37, a piano wire 41 is fixed between the upper surface of the ring-type displacement meter 40 and the lower surface of the main girder G directly above it, and the ring-type displacement meter 40 measures the displacement of the piano wire 41 due to the deflection of the upper structure 7 and transmits the measured target data U(k) to the measurement device 1. The measurement device 1 generates measurement data U'(k) by removing drift noise from the target data U(k) transmitted from the ring-type displacement meter 40. In the measurement system 10 shown in FIG. 38, a camera 50 transmits an image of a target 51 provided on the side of the main girder G to the measurement device 1. The measuring device 1 processes the image transmitted from the camera 50, calculates the displacement of the target 51 due to the deflection of the upper structure 7, generates target data U(k), and generates measurement data U'(k) by removing drift noise from the generated target data U(k). In the example of FIG. 38, the measuring device 1 generates the target data U(k) as a displacement measuring device using image processing, but a displacement measuring device (not shown) different from the measuring device 1 may generate the target data U(k) using image processing.

In addition, in each of the above embodiments, the bridge 5 is a railway bridge, and the moving body moving on the bridge 5 is a railway vehicle 6, but the bridge 5 may be a road bridge, and the moving body moving on the bridge 5 may be a vehicle such as an automobile, a tram, or a construction vehicle. FIG. 39 shows an example of the configuration of the measurement system 10 when the bridge 5 is a road bridge and a vehicle 6a moves on the bridge 5. In FIG. 39, the same components as in FIG. 1 are given the same reference numerals. As shown in FIG. 39, the bridge 5, which is a road bridge, is composed of a superstructure 7 and a substructure 8, just like a railway bridge. FIG. 40 is a cross-sectional view of the superstructure 7 cut along line A-A in FIG. 39. As shown in FIGS. 39 and 40, the superstructure 7 includes a bridge deck 7a composed of a deck plate F, a main girder G, a cross girder (not shown), and a support 7b. As shown in FIG. 39, the substructure 8 includes a pier 8a and an abutment 8b. The superstructure 7 is a structure that spans either adjacent abutments 8b and piers 8a, two adjacent abutments 8b, or two adjacent piers 8a. Both ends of the superstructure 7 are located at the positions of adjacent abutments 8b and piers 8a, two adjacent abutments 8b, or two adjacent piers 8a. The bridge 5 is, for example, a steel bridge, a girder bridge, or an RC bridge.

Each sensor 2 is installed in the longitudinal center of the superstructure 7, specifically, in the longitudinal center of the main girder G. However, as long as each sensor 2 can detect acceleration to calculate the displacement of the superstructure 7, its installation position is not limited to the center of the superstructure 7. If each sensor 2 is installed on the deck F of the superstructure 7, there is a risk that it will be destroyed by the running vehicle 6a, and there is also a risk that the measurement accuracy will be affected by local deformation of the bridge deck 7a. Therefore, in the examples of Figures 39 and 40, each sensor 2 is installed on the main girder G of the superstructure 7.

As shown in FIG. 40, the superstructure 7 has two lanes L ₁ and L ₂ and three main girders G on which the vehicle 6a, which is a moving body, can move. In the example of FIG. 39 and FIG. 40, a sensor 2 is provided on each of the two main girders at both ends in the center of the longitudinal direction of the superstructure 7, and an observation point R ₁ is provided on the surface of the lane L ₁ located vertically above one sensor 2, and an observation point R ₂ is provided on the surface of the lane L ₂ located vertically above the other sensor 2. That is, the two sensors 2 are observation devices that observe the observation points R ₁ and R ₂ , respectively. The two sensors 2 that observe the observation points R ₁ and R ₂ , respectively, may be provided at positions that can detect the acceleration generated at the observation points R ₁ and R ₂ due to the running of the vehicle 6a, but are preferably provided at positions close to the observation points R ₁ and R _2. The number and installation positions of the sensors 2 and the number of lanes are not limited to the examples shown in FIG. 39 and FIG. 40, and various modifications are possible.

The measuring device 1 calculates the displacement of the flexure of the lanes _L1 , _L2 caused by the travel of the vehicle 6a based on the acceleration data output from each sensor 2, and transmits information on the displacement of the lanes _L1 , _L2 to the monitoring device 3 via the communication network 4. The monitoring device 3 stores the information in a storage device (not shown) and may perform processing such as monitoring the vehicle 6a and determining abnormalities in the superstructure 7 based on the information.

In addition, in each of the above embodiments, each sensor 2 is provided on the main girder G of the superstructure 7, but it may also be provided on the surface or inside of the superstructure 7, on the underside of the deck F, on the pier 8a, etc. In addition, in each of the above embodiments, the superstructure of a bridge is given as an example of a structure, but this is not limited thereto, and the structure may be anything that deforms due to the movement of a moving object.

Railroad cars or vehicles passing over bridges are heavy vehicles that can be measured using BWIM. BWIM is an abbreviation for Bridge Weigh in Motion, and is a technology that measures the weight and number of axles of railroad cars or vehicles passing over bridges by treating the bridge as a "scale" and measuring the deformation of the bridge. The superstructure of a bridge, which can analyze the weight of a running railroad car or vehicle from responses such as deformation and strain, is a structure in which BWIM functions, and the BWIM system, which applies the physical process between the action on the bridge superstructure and the response, makes it possible to measure the weight of a running vehicle.

The above-described embodiment and modified examples are merely examples, and the present invention is not limited to these. For example, each embodiment and each modified example can be appropriately combined.

The present invention includes configurations that are substantially the same as the configurations described in the embodiments, for example configurations with the same functions, methods and results, or configurations with the same purpose and effect. The present invention also includes configurations in which non-essential parts of the configurations described in the embodiments are replaced. The present invention also includes configurations that achieve the same effects as the configurations described in the embodiments, or configurations that can achieve the same purpose. The present invention also includes configurations in which publicly known technology is added to the configurations described in the embodiments.

The following can be derived from the above-described embodiment and variant examples:

One aspect of the measurement method is
a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

In this measurement method, vibration component reduced data in which the vibration component is reduced and vibration component data including the vibration component are generated using the target data to be processed, drift noise reduced data in which the drift noise is reduced is generated from the vibration component reduced data, and correction data is estimated based on the drift noise reduced data. Since the vibration component is reduced in the drift noise reduced data, correction data estimated with high accuracy is obtained. Since the correction data corresponds to the difference between the data obtained by removing the drift noise from the vibration component reduced data and the drift noise reduced data, it contains significant signal components removed by the high-pass filter processing. Therefore, according to this measurement method, by adding the drift noise reduced data, the correction data, and the vibration component data, it is possible to generate measurement data in which the drift noise is reduced with respect to the target data. Also, according to this measurement method, by using the target data to be processed, drift noise reduced data, correction data, and vibration component data are generated, and by adding the drift noise reduced data, the correction data, and the vibration component data, it is possible to generate measurement data in which the drift noise is reduced without having to prepare information for reducing the drift noise in advance. Therefore, by using this measurement method, it is possible to obtain accurate measurement data regardless of changes in the environment, while also reducing costs.

In one embodiment of the measurement method,
The correction data estimation step includes:
a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a first section correction data generating step of generating first section correction data by inverting a sign of the drift noise reduced data in the first section;
a second interval correction data generating step of adding, before a predetermined time in the second interval, data obtained by rearranging the drift noise reduction data before the first peak in reverse order to data after the first peak, and straight line data obtained by multiplying a straight line passing through the first peak and the second peak by −2, and adding, after the predetermined time in the second interval, data obtained by rearranging the drift noise reduction data after the second peak in reverse order to data before the second peak, and the straight line data, to generate second interval correction data;
a third section correction data generating step of generating third section correction data by inverting a sign of the drift noise reduction data in the third section;
The method may further include a correction data generating step of generating the correction data by adding the first interval correction data, the second interval correction data, and the third interval correction data.

This measurement method identifies three sections based on the characteristics of drift noise reduced data that reduces drift noise and vibration components from the target data, and can generate more appropriate correction data for each section, thereby improving the estimation accuracy of the generated correction data.

Another aspect of the measurement method is
a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced;
a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data in the second section, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
The method includes a measurement data generating process for generating measurement data by using the first section as the vibration component reduced data, adding the drift noise reduced data, the correction data and the vibration component data in the second section, and using the third section as the vibration component reduced data.

In this measurement method, vibration component reduced data in which the vibration component is reduced and vibration component data including the vibration component are generated using the target data to be processed, drift noise reduced data in which the drift noise is reduced is generated from the vibration component reduced data, three sections are identified based on the characteristics of the drift noise reduced data, and correction data is estimated in the second section. Since the drift noise reduced data has reduced vibration components, correction data estimated with high accuracy is obtained in the second section. In addition, since the correction data corresponds to the difference between the data obtained by removing the drift noise from the vibration component reduced data and the drift noise reduced data in the second section, it contains significant signal components removed by the high-pass filter processing. Therefore, according to this measurement method, the first and third sections are vibration component data, and in the second section, the drift noise reduced data, the correction data, and the vibration component data are added to generate measurement data in which the drift noise is reduced with respect to the target data. Furthermore, according to this measurement method, drift noise reduction data, correction data, and vibration component data are generated using the target data to be processed, and the drift noise reduction data, correction data, and vibration component data are added in the second section, thereby generating measurement data with reduced drift noise without having to prepare information for reducing drift noise in advance. Therefore, by using this measurement method, it is possible to obtain accurate measurement data regardless of changes in the environment, and to reduce costs.

In addition, with this measurement method, in order to generate measurement data, it is not necessary to generate correction data or add drift noise reduction data, correction data, and vibration component data in the first and third sections, so the amount of calculations is reduced.

In one embodiment of the measurement method,
In the correction data estimation step,
Before a predetermined time in the second section, data obtained by rearranging the drift noise reduction data before the first peak in reverse order to data after the first peak and straight-line data obtained by multiplying a straight line passing through the first peak and the second peak by −2 are added together, and after the predetermined time in the second section, data obtained by rearranging the drift noise reduction data after the second peak in reverse order to data before the second peak and the straight-line data are added together to generate the correction data.

This measurement method makes it possible to generate more appropriate correction data in the second section based on the characteristics of the drift noise reduction data, which reduces drift noise and vibration components compared to the target data, thereby improving the estimation accuracy of the generated correction data.

In one embodiment of the measurement method,
In the low pass filter processing step,
The target data may be subjected to a fast Fourier transform to calculate a fundamental frequency, and the vibration component reduced data may be generated by performing a moving average process on the target data in a period corresponding to the fundamental frequency as the low-pass filter process.

In this measurement method, the moving average process not only requires a small amount of calculation, but also has a very large attenuation of the fundamental frequency signal component and its harmonic components, so vibration component reduced data in which vibration components are effectively reduced can be obtained. Therefore, with this measurement method, the influence of vibration components can be eliminated and the estimation accuracy of correction data can be improved.

In one embodiment of the measurement method,
In the low pass filter processing step,
The target data may be subjected to a fast Fourier transform process to calculate a fundamental frequency, and as the low-pass filter process, an FIR filter process may be performed on the target data to attenuate signal components having frequencies equal to or higher than the fundamental frequency, thereby generating the vibration component reduced data.

In this measurement method, the FIR filter process requires a larger amount of calculation than the moving average process, but it can attenuate all signal components with frequencies equal to or higher than the fundamental frequency. Therefore, this measurement method can eliminate the effects of vibration components equal to or higher than the fundamental frequency, improving the estimation accuracy of the correction data.

In one embodiment of the measurement method,
The high-pass filter process may be a process of subtracting data obtained by performing moving average processing or FIR filter processing on the vibration component reduced data from the vibration component reduced data.

This measurement method allows high-pass filter processing to be performed easily, and since the group delay of each signal component contained in the vibration component reduction data is constant in moving average processing or FIR filter processing, correction data can be estimated with high accuracy.

In one embodiment of the measurement method,
The target data may be data on the displacement of a structure caused by a moving object moving across the structure.

With this measurement method, displacement data of a structure caused by the movement of a moving object is obtained as measurement data with reduced drift noise, making it possible to measure the displacement of the structure with high accuracy.

In one embodiment of the measurement method,
The target data may be data obtained by integrating twice the acceleration in a direction intersecting with the plane of the structure in which the moving body moves.

This measurement method allows the displacement of a structure to be measured with high accuracy using output data from an acceleration sensor installed in the structure.

In one embodiment of the measurement method,
The target data may be observation data from a contact type displacement meter, a ring type displacement meter, a laser displacement meter, a pressure sensor, a displacement measuring device using image processing, or a displacement measuring device using optical fiber, or data obtained by integrating the speed detected by a speed sensor.

This measurement method allows the displacement of a structure to be measured with high accuracy using displacement, stress change, or velocity data.

In one embodiment of the measurement method,
The structure may be a bridge superstructure.

This measurement method allows the displacement of the bridge's superstructure to be measured with high accuracy.

In one embodiment of the measurement method,
The frequency of the drift noise may be lower than the minimum natural vibration frequency of the superstructure.

According to this measurement method, by setting the cutoff frequency of the low-pass filter process and the high-pass filter process higher than the frequency of the drift noise of the superstructure and lower than the minimum value of the natural vibration frequency, it is possible to reduce the drift noise in the generated displacement data without reducing the signal component of the natural vibration frequency of the superstructure and its harmonic components.

In one embodiment of the measurement method,
The moving object may be a vehicle or a railcar.

This measurement method makes it possible to accurately measure the displacement of a structure caused by the movement of a vehicle or railcar.

In one embodiment of the measurement method,
The target data may include data of a waveform that is convex in a positive or negative direction.

This measurement method allows more appropriate correction data to be generated based on the characteristics of a waveform that is convex in the positive or negative direction, thereby improving the estimation accuracy of the generated correction data.

In one embodiment of the measurement method,
The waveform may be a square waveform, a trapezoidal waveform or a half-sine waveform.

This measurement method allows more appropriate correction data to be generated based on the characteristics of a rectangular waveform, trapezoidal waveform, or half-sine waveform, thereby improving the estimation accuracy of the generated correction data.

One aspect of the measurement device is
a low-pass filter processing unit that performs low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing unit that generates drift noise reduced data by high-pass filtering the vibration component reduced data to reduce the drift noise;
a correction data estimation unit that estimates correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating unit that generates vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
and a measurement data generating unit that generates measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

This measurement device generates vibration component reduced data in which vibration components are reduced and vibration component data including vibration components using target data to be processed, generates drift noise reduced data in which drift noise is reduced from the vibration component reduced data, and estimates correction data based on the drift noise reduced data. Since the vibration components are reduced in the drift noise reduced data, correction data estimated with high accuracy can be obtained. Since the correction data corresponds to the difference between the data obtained by removing drift noise from the vibration component reduced data and the drift noise reduced data, it contains significant signal components removed by high-pass filter processing. Therefore, according to this measurement device, by adding the drift noise reduced data, the correction data, and the vibration component data, it is possible to generate measurement data in which drift noise is reduced compared to the target data. Also, according to this measurement device, by using the target data to be processed to generate drift noise reduced data, correction data, and vibration component data, and by adding the drift noise reduced data, correction data, and vibration component data, it is possible to generate measurement data in which drift noise is reduced without having to prepare information for reducing drift noise in advance. Therefore, by using this measuring device, it is possible to obtain accurate measurement data regardless of changes in the environment, while also reducing costs.

One aspect of the measurement system is
An embodiment of the measuring device;
An observation device for observing an observation point,
The target data is data based on observation data obtained by the observation device.

One aspect of the measurement program is
a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of processing the vibration component reduced data through a high-pass filter to generate drift noise reduced data in which the drift noise is reduced;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by removing the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
and generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data.

In this measurement program, vibration component reduced data in which vibration components are reduced and vibration component data including vibration components are generated using target data to be processed, drift noise reduced data in which drift noise is reduced is generated from the vibration component reduced data, and correction data is estimated based on the drift noise reduced data. Since the vibration components are reduced in the drift noise reduced data, correction data estimated with high accuracy is obtained. Since the correction data corresponds to the difference between the data in which drift noise is removed from the vibration component reduced data and the drift noise reduced data, it contains significant signal components removed by the high-pass filter processing. Therefore, according to this measurement program, measurement data in which drift noise is reduced with respect to the target data can be generated by adding the drift noise reduced data, the correction data, and the vibration component data. Also, according to this measurement program, drift noise reduced data, correction data, and vibration component data are generated using target data to be processed, and measurement data in which drift noise is reduced can be generated by adding the drift noise reduced data, the correction data, and the vibration component data. Therefore, by using this measurement program, it is possible to obtain accurate measurement data regardless of changes in the environment, while also reducing costs.

1...measuring device, 2...sensor, 3...monitoring device, 4...communication network, 5...bridge, 6...railroad vehicle, 6a...vehicle, 7...superstructure, 7a...bridge deck, 7b...bearing, 7c...rail, 7d...sleeper, 7e...ballast, F...floor plate, G...main girder, 8...substructure, 8a...pier, 8b...abutment, 10...measuring system, 11...first communication unit, 12...second communication unit, 13...processor, 14...memory unit, 21...communication unit, 22...acceleration sensor, 23...processor, 24...memory unit, 31...communication unit, 32...processor, 33...display unit, 34...operation unit, 35...memory unit, 40... Ring-type displacement meter, 41... piano wire, 50... camera, 51... target, 131... target data generation unit, 132... low-pass filter processing unit, 133... high-pass filter processing unit, 134... correction data estimation unit, 135... vibration component data generation unit, 136... measurement data generation unit, 137... measurement data output unit, 138... section identification unit, 141... measurement program, 142... observation data, 143... measurement data, 241... observation program, 242... observation data, 321... measurement data acquisition unit, 322... monitoring unit, 351... monitoring program, 352... measurement data string

Claims (18)

a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of generating drift noise reduced data in which the drift noise is reduced by high-pass filtering the vibration component reduced data;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by subtracting the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
a measurement data generating step of generating measurement data by adding the drift noise reduced data, the correction data, and the vibration component data,
The correction data estimation step includes:
a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a first section correction data generating step of generating first section correction data by inverting a sign of the drift noise reduced data in the first section;
Before a predetermined time in the second section, data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak, and data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak,
a second section correction data generating step of adding straight line data obtained by multiplying a straight line passing through the second peak and the second peak by −2, and adding the straight line data to data obtained by rearranging the drift noise reduction data after the second peak in reverse order to data before the second peak after the predetermined time in the second section, to generate second section correction data;
a third section correction data generating step of generating third section correction data by inverting a sign of the drift noise reduction data in the third section;
a correction data generating step of generating the correction data by adding the first interval correction data, the second interval correction data, and the third interval correction data. a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of generating drift noise reduced data in which the drift noise is reduced by high-pass filtering the vibration component reduced data;
a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by subtracting the drift noise from the vibration component reduced data in the second section based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
The first section is the vibration component data , the second section is the drift noise reduction data, the correction data, and the vibration component data are added, and the third section is the vibration
A measurement data generating step of generating measurement data as component data ,
In the correction data estimation step,
Before a predetermined time in the second section, data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak, and data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak,
and adding straight-line data obtained by multiplying a straight line passing through the second peak by -2, and then adding, after the predetermined time in the second section, data obtained by rearranging the drift noise reduced data after the second peak in reverse order to data before the second peak, to the straight-line data, to generate the correction data. In claim 1 or 2 ,
In the low pass filter processing step,
A measurement method comprising: performing fast Fourier transform processing on the target data to calculate a fundamental frequency; and performing moving average processing on the target data at a period corresponding to the fundamental frequency as the low-pass filter processing to generate the vibration component reduced data. In claim 1 or 2 ,
In the low pass filter processing step,
A measurement method comprising: performing fast Fourier transform processing on the target data to calculate a fundamental frequency; and performing FIR filter processing on the target data as the low-pass filter processing, which attenuates signal components having frequencies equal to or higher than the fundamental frequency, to generate the vibration component reduced data. In any one of claims 1 to 4,
The measurement method, wherein the high-pass filter processing is processing for subtracting data obtained by subjecting the vibration component reduced data to moving average processing or FIR filter processing from the vibration component reduced data. In any one of claims 1 to 5,
The target data is data of a displacement of the structure caused by a moving object moving the structure.
Measurement method. In claim 6,
The target data is the acceleration in a direction intersecting the plane of the structure along which the moving body moves, expressed as
A measurement method in which the data is integrated multiple times. In claim 6,
The target data includes a contact type displacement meter, a ring type displacement meter, a laser displacement meter, a pressure sensor,
A measurement method in which the data is obtained by integrating observation data from a displacement measuring device using image processing or a displacement measuring device using optical fiber, or data obtained by integrating the speed detected by a speed sensor. In any one of claims 6 to 8,
The measurement method, wherein the structure is a superstructure of a bridge. In claim 9,
The frequency of the drift noise is lower than the minimum natural vibration frequency of the upper structure.
Measurement method. In any one of claims 6 to 10,
The measuring method, wherein the moving object is a vehicle or a railway vehicle. In any one of claims 1 to 11,
A measurement method, wherein the target data includes data of a waveform that is convex in a positive or negative direction. In claim 12,
The measurement method, wherein the waveform is a rectangular waveform, a trapezoidal waveform, or a half-sine waveform. a low-pass filter processing unit that performs low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing unit that generates drift noise reduced data in which the drift noise is reduced by high-pass filtering the vibration component reduced data;
a correction data estimation unit that estimates correction data corresponding to a difference between the drift noise reduced data and data obtained by subtracting the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating unit that generates vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
a measurement data generating unit that generates measurement data by adding the drift noise reduction data, the correction data, and the vibration component data, and the correction data estimating unit includes:
calculating a first peak and a second peak of the drift noise reduced data, and identifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
Inverting a sign of the drift noise reduction data in the first section to generate first section correction data;
Before a predetermined time in the second section, data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak, and data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak,
and adding straight line data obtained by multiplying a straight line passing through the second peak by −2, and adding data obtained by rearranging the drift noise reduced data after the second peak in reverse order to data before the second peak after the predetermined time in the second section to the straight line data to generate second section correction data;
Inverting a sign of the drift noise reduction data in the third section to generate third section correction data;
A measurement device that generates the correction data by adding the first interval correction data, the second interval correction data, and the third interval correction data. a low-pass filter processing unit that performs low-pass filter processing on target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing unit that generates drift noise reduced data in which the drift noise is reduced by high-pass filtering the vibration component reduced data;
a section specifying unit that calculates a first peak and a second peak of the drift noise reduced data and specifies a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a correction data estimation unit that estimates correction data corresponding to a difference between the drift noise reduced data and data obtained by subtracting the drift noise from the vibration component reduced data in the second section based on the drift noise reduced data;
a vibration component data generating unit that generates vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
The first section is the vibration component data , the second section is the drift noise reduction data, the correction data, and the vibration component data are added, and the third section is the vibration
a measurement data generating unit that generates measurement data as component data ;
The correction data estimation unit
Before a predetermined time in the second section, data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak, and data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak,
and adding straight-line data obtained by multiplying a straight line passing through the second peak by -2, and adding, after the predetermined time in the second section, data obtained by rearranging the drift noise reduced data after the second peak in reverse order to data before the second peak, to the straight-line data, to generate the correction data. The measuring device according to claim 14 or 15,
An observation device for observing an observation point,
A measurement system, wherein the target data is data based on observation data obtained by the observation device. a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of generating drift noise reduced data in which the drift noise is reduced by high-pass filtering the vibration component reduced data;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by subtracting the drift noise from the vibration component reduced data, based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
a measurement data generating step of generating measurement data by adding the drift noise reduction data, the correction data, and the vibration component data;
The correction data estimation step includes:
a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a first section correction data generating step of generating first section correction data by inverting a sign of the drift noise reduced data in the first section;
Before a predetermined time in the second section, data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak, and data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak,
a second section correction data generating step of adding straight line data obtained by multiplying a straight line passing through the second peak and the second peak by −2, and adding the straight line data to data obtained by rearranging the drift noise reduction data after the second peak in reverse order to data before the second peak after the predetermined time in the second section, to generate second section correction data;
a third section correction data generating step of generating third section correction data by inverting a sign of the drift noise reduction data in the third section;
a correction data generating step of generating the correction data by adding the first interval correction data, the second interval correction data, and the third interval correction data. a low-pass filter processing step of low-pass filtering target data including drift noise and vibration components to generate vibration component reduced data in which the vibration components are reduced;
a high-pass filter processing step of generating drift noise reduced data in which the drift noise is reduced by high-pass filtering the vibration component reduced data;
a section specifying step of calculating a first peak and a second peak of the drift noise reduced data and specifying a first section before the first peak, a second section between the first peak and the second peak, and a third section after the second peak;
a correction data estimating step of estimating correction data corresponding to a difference between the drift noise reduced data and data obtained by subtracting the drift noise from the vibration component reduced data in the second section based on the drift noise reduced data;
a vibration component data generating step of generating vibration component data including the vibration component by subtracting the vibration component reduced data from the target data;
The first section is the vibration component data , the second section is the drift noise reduction data, the correction data, and the vibration component data are added, and the third section is the vibration
a measurement data generating step of generating measurement data as component data ;
In the correction data estimation step,
Before a predetermined time in the second section, data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak, and data obtained by rearranging the drift noise reduced data before the first peak in reverse order to data after the first peak,
and adding straight-line data obtained by multiplying a straight line passing through the second peak by -2, and adding, after the predetermined time in the second section, data obtained by rearranging the drift noise reduced data after the second peak in reverse order to data before the second peak, to the straight-line data, to generate the correction data.

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