JPH0569461B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a method and apparatus for diagnosing an exercise device having a sliding part, such as a rotating device or a swinging device.

[Conventional technology]

Examples of sliding motion devices include compressors used in air conditioners and refrigerators, but in order to inspect the operating condition of the sliding parts of these compressors, it is common practice to inspect them after disassembly work. Ta. Additionally, a method of identifying abnormalities acoustically was often used by skilled inspectors. However, dismantling inspections are uneconomical in terms of cost and time, and hearing tests lack accuracy, so an accurate inspection method that does not require disassembly is desired.

On the other hand, for example, Japanese Patent Laid-Open No. 58-176545 discloses a method of determining an abnormality based on the results of time and frequency analysis of an output signal using a vibration detector. This method detects the vibration signal of motion within one cycle, sets a limit for the reference pattern using a time characteristic pattern and a frequency characteristic pattern, and determines that a pattern that exceeds the limit is abnormal. It is.

[Problem that the invention seeks to solve]

The conventional determination method described above uses the entire pattern, so it is very difficult to determine the limits of the reference pattern, and it has the disadvantage that it is easily influenced by operating conditions and measurement conditions. Furthermore, even if it is possible to determine whether the condition is normal or abnormal, it is difficult to determine whether the condition is abnormal. Therefore, although this vibration detection method is said to be effective for detecting defects such as omissions in work and the inclusion of foreign objects, it is inappropriate for detecting abnormalities and damaged states of sliding parts.

An object of the present invention is to provide a method for accurately and quickly diagnosing abnormalities and damaged states of sliding parts in motion devices such as rotating devices and swinging devices, and a device suitable for carrying out the method. It is in.

[Means for solving problems]

Acoustic emission (hereinafter abbreviated as AE) is a means to achieve the above purpose.
We propose a method using measurement methods. In other words, we focused on the fact that the sliding motion device constantly generates sudden AE waves in synchronization with its sliding motion, and by comparing the sudden high values with the continuous high values, the sliding part can be evaluated. Abnormalities are determined, and furthermore, the strength of the rotational tuning component obtained from the frequency analysis results of AE detection is used to diagnose the damage state of the sliding part.

Furthermore, a diagnostic device suitable for carrying out this diagnostic method is constructed, and when an abnormality is determined, the sliding motion or load is reduced to reduce the spread of damage.

In addition, in the present invention, a signal that is at least proportional to the continuous wave peak value includes not only a signal of the peak value of only continuous waves excluding sudden waves, but also the effective value of the entire waveform during one cycle including sudden waves. include.

[Effect]

In normal operation, the signal strength of the continuous wave with respect to the sudden peak value is low, but if an abnormality occurs in the sliding part, the sudden peak value decreases and the signal strength of the continuous wave increases. In addition, the frequency spectrum of AE detection when the sliding part has abrasive damage has a high sliding period tuning component, and the frequency spectrum when the sliding part has abrasive damage shows a rotational tuning component. It won't happen. Therefore, abnormalities can be diagnosed by comparing signals proportional to the peak values of sudden waves and continuous waves, and damage conditions can also be identified by comparing the strength of the sliding period tuning component and the full frequency component. becomes.

〔Example〕

Hereinafter, the present invention will be explained in detail using illustrative examples.

In the embodiment, a rotary compressor is taken as an example of a sliding motion device, and a case will be described in which a sliding synchronization signal is detected by a rotation detector.

FIG. 1 is an overall configuration diagram of a rotary compressor diagnostic apparatus according to an embodiment of the present invention. In the figure, an AE sensor 2 is installed in a compressor 1, and the output of the AE sensor 2 is amplified by an amplifier 3 and then sent to an envelope detector 4.
The signal is detected and input to the waveform parameter analysis section 5. Further, a rotation signal is obtained by the rotation detector 6 and inputted to the waveform parameter analysis section 5. An abnormality determination section 7 uses the output of the waveform parameter analysis section 5 to determine an abnormality, and outputs the result to a display device 8 .

Next, the structure of the rotary compressor and the damaged parts due to sliding will be explained using FIG. 2. Figure 2 shows the structure of a rotary compressor. In the figure, the crankshaft 9 is supported by an upper bearing 10 and a lower bearing 11, and is rotated in the direction of arrow 12 by the drive of a motor, so that the refrigerant is adiabatically compressed in the compression chamber 14 of the cylinder 13. and becomes high-pressure gas. The following are the main areas where the compressor slides in such a structure.

(1) Sliding area between crankshaft 9 and upper bearing 10 (2) Sliding area between crankshaft 9 and lower bearing 11 (3) Sliding area between vane 15 and roller 16 (4) Roller 16 and crank Sliding parts with the shaft 9 If these sliding parts are damaged, the efficiency of the compressor will decrease, and in the worst case, it will become impossible to rotate.

Figure 3 shows an example of the AE generation characteristics (detected signal) of a rotary compressor. Figure a shows an example of the AE characteristics of a normal product, and Figures b and c show examples of the AE characteristics of a product with damaged sliding parts. As shown in Figure a, the normal product is characterized in that only large-amplitude burst-type AE waves 17 that are synchronized with the rotation signal are generated. This sudden type AE wave is also called a stationary AE wave because it occurs regularly. Various factors may be considered for generating the stationary AE wave 17 depending on the case, but for example, it is a collision sound generated at the top dead center of the vane 15. Figure b shows the waveform when there is a scratch on the bearing, in which the level of the steady AE wave 17 decreases and is replaced by a continuous AE wave (abnormal AE wave 17).
It is characterized by the generation of waves) 18. Figure c shows the AE waveform when the tip of the vane is damaged due to abrasion.

The operation shown in FIG. 1 will be explained using the above AE characteristics. FIG. 4 is a flowchart for explaining the operation of FIG. 1. The AE detection signal 19 is subjected to parameter analysis using the rotation signal 20 only during a fixed period _Ts of one cycle or more (step 21). As parameters, the average wave height V _p of sudden waves and the effective signal strength V _n including continuous waves are determined. The effective signal strength V _n refers to the average value, effective value, or energy value between T _s including sudden waves and continuous waves. Next, as shown in Figure 3,
Using this V _n , the threshold value V _th is determined as shown in the following equation.

V _th = K・V _n ...(1) Here, K is a proportionality constant and is generally selected to be 1 or more. Next, the wave height average value V _p is determined using the following equation, with those exceeding the threshold value V _th as AE events.

V _p =1/N _N 〓 ⁱ⁼¹ V _p ...(2) Here, N is the number of AE events.

Returning to FIG. 4, as the first step of abnormality determination, a steady wave number determination is performed (step 22). K in equation (1)
Set to a value as close to 1 as possible, and
If the number N of AE events falling within T _s is equal to or one less than the rotation period number, it is determined to be normal; otherwise, the process proceeds to the second stage of abnormality determination.

As the second stage of abnormality determination, the ratio of the effective signal strength V _n to the average peak value V _p is taken, and if the value is equal to or smaller than the constant value K _s as shown in the following formula, it is determined to be normal. , _Ks , it is determined to be abnormal (step 23).

V _n /V _p K _s (3) Here, the constant value K _s is a statistically determined constant.

By the above method, it is possible to determine whether it is normal (step 24) or abnormal (step 25).

As a next example, a method for diagnosing an abnormal state will be described with reference to FIG. In Fig. 5, up to the display of normal and abnormal results, it is essentially the same as Fig. 4, but the feature is that frequency analysis (FFT) is added at the parameter analysis (step 26). . Then, for those determined to be abnormal, the damage form is determined using the frequency analysis results (steps 26 to 28). When the sum of the intensity of the frequency analysis results over all frequencies is A _FT , and the intensity at the rotational frequency component is εA _FR , A _FR and
If the ratio of A to _FT is greater than the standard value K _F as shown in the following formula, it is determined that the damage is due to abrasion (Step 28)
However, if _KF is not reached, it is determined that the damage is an abrasion (Step 27).

A _FR /A _FT K _F ...(4) For example, the frequency analysis results of the abrasion damage signal shown in Figure 3b and the abrasive damage signal shown in Figure 3c are as shown in Figures 6a and b, respectively. become that way. As can be seen from FIG. 6, the intensity at the rotational frequency _R is remarkable in b in the figure, whereas no particularly large value is shown in a. Therefore, the reference value K _F in equation (4) is a value that is determined statistically, but is a value that can be sufficiently identified.

Next, a method for further improving the accuracy of the above-described diagnostic method will be described with reference to FIG. Fourth
This is a method of performing averaging processing 29 before performing parameter analysis 21, 26 of the AE detection signal shown in FIGS. A rotation signal is used as the timing signal. By performing averaging processing n times using the rotation signal as a timing signal in this manner, so-called background noise can be reduced to 1/n.

Still another embodiment will be described with reference to FIGS. 8 and 9. Figures 8 and 9 show that when an abnormality is determined by the diagnostic equipment and method described above, recovery from damage or other abnormalities is expected by reducing the load on the sliding parts during operation. The purpose is to FIG. 8 shows a method of extracting the diagnosis result of the AE diagnosis device 29 as a control signal 30 and inputting it to the rotation control section 31 of the drive device 32.
In this case, if the AE diagnosis device 29 outputs an abnormal result, the control signal 30 performs control to lower the rotation speed. Further, in FIG. 9, a control signal 30 of the AE diagnostic device 29 is applied to the adjustment section 35 of the load 30. For example, when an abnormality diagnosis result is output, control is performed to reduce the load. Therefore, according to FIGS. 8 and 9, the sliding of the exercise device 33 and the load 34 can be reduced, and recovery from damage can be expected.

〔Effect of the invention〕

According to the present invention, it is possible to diagnose an abnormality or a damaged state in a sliding portion of a device that repeatedly slides periodically, such as a compressor, with high accuracy and in a short time without dismantling the device. Therefore, the industrial
The economic effect is large, and it also has a significant effect on ensuring safe operation of equipment.

Furthermore, according to the present diagnostic device, it can be easily configured and it is possible to suppress the spread of damage to the sliding portion.

[Brief explanation of drawings]

Fig. 1 shows an embodiment of the present invention, Fig. 2 shows the structure of a rotary compressor for explaining the specific application of Fig. 1, and Fig. 3 shows a specific example of a rotary compressor.
AE generation characteristics, Fig. 4 is a flowchart for explaining the operation of Fig. 1, Fig. 5 is a flowchart for explaining the method of diagnosing a damage state according to the present invention, Fig. 6 is the frequency of the AE detection signal at the time of damage. Analysis result example, Fig. 7 is another example for improving the accuracy of the present invention, Fig. 8
9 and 9 respectively show an embodiment in which the diagnostic results of the present invention are applied to control. 1... Compressor, 2... AE sensor, 6... Rotation detector.

Claims (1)

[Claims] 1. An exercise device that repeats periodic sliding motion, and an abnormality in the exercise device is diagnosed using a signal from an AE sensor installed in the device and a periodic signal representing the sliding period of the exercise device. A constantly occurring sudden wave is determined from the above-mentioned AE sensor signal and the periodic signal, and a signal proportional to the peak value of the sudden wave is determined,
A method for diagnosing a sliding motion device, comprising diagnosing an abnormality in a sliding portion of the motion device by comparing the magnitude with at least a signal proportional to the peak value of a continuous wave in the AE sensor signal. 2. In claim 1, the magnitude of the two signals is compared by determining the crest factor of the signal proportional to the crest value of the sudden wave with respect to the signal proportional to the crest value of the continuous wave; A method for diagnosing a sliding motion device, characterized in that a diagnosis is made by comparing and setting values. 3 In claim 1, the signal proportional to the peak value of the sudden wave and the signal proportional to the peak value of the continuous wave are the peak values of the sudden wave and standing wave after envelope detection of the AE sensor signal. 1. A method for diagnosing a sliding motion device, characterized by calculating the average of the respective values. 4. In a motion device that repeats periodic sliding motion, in which an abnormality in the motion device is diagnosed using a signal from an AE sensor installed in the device and a periodic signal representing the sliding period of the motion device, the AE The damage state of the sliding portion of the exercise device is determined by comparing the intensity of a component of the sensor signal tuned to the synchronization signal with the intensity of all frequency components in the AE sensor signal. Diagnosis method for sliding motion devices. 5. In claim 4, the comparison of the intensities determines the ratio of the intensity of the tuned component to the intensity of the full frequency component, and when the ratio is greater than or equal to a predetermined value, wear damage of the sliding part is detected; A method for diagnosing a sliding motion device, characterized in that when the ratio is less than or equal to a predetermined value, it is determined that the sliding portion has abrasion damage. 6. A motion device that repeats periodic sliding motion, a load of the device, an AE sensor installed in the motion device, and a period detector that outputs a sliding periodic signal of the motion device. , means for envelope detection processing the AE sensor signal, inputting the envelope detection signal and the sliding period signal, and calculating a parameter proportional to the peak value of the sudden wave and a parameter proportional to the peak value of the continuous wave. A sliding motion device comprising means for analyzing waveform parameters to be obtained, and means for inputting parameters proportional to the wave height values of the sudden waves and continuous waves and determining an abnormality in the sliding portion of the motion device. diagnostic equipment. 7 In claim 6, the waveform parameter analysis means includes a parameter representing the strength of a component of the envelope detection signal that is tuned to the synchronization signal, and a parameter representing the strength of a component of the envelope detection signal that is tuned to the synchronization signal, and The abnormality determining means determines the abnormality from a parameter proportional to the peak values of the sudden wave and the continuous wave, and then determines the intensity of the synchronization signal tuning component and the intensity of all frequency components. A diagnostic device for a sliding motion device, characterized in that the diagnostic device is configured to determine a damage state of the sliding portion from a parameter representing. 8 In claim 6 or 7,
A diagnostic device for a sliding motion device, wherein the motion device is configured to suppress the sliding motion period in response to an abnormality determination signal from the abnormality determination means. 9 In claim 6 or 7,
A diagnostic device for a sliding motion device, characterized in that the load is configured to be reduced in response to an abnormality determination signal from the abnormality determination means.