JP7540120B2 - Defect Inspection Equipment - Google Patents

Description

This application relates to a defect inspection apparatus.

The rotor wedge of a turbine generator is a part that prevents the rotor coil from flying out of the rotor due to centrifugal force, and defects can occur inside it due to the centrifugal force during operation. If operation is continued with a defect left unchecked, the defect will grow and may lead to damage to the generator, so regular inspection is necessary.

Currently, these defects are detected by ultrasonic testing, but to shorten the inspection time, there is a demand for technology that allows inspection without disassembling the generator. However, there are some generators that cannot be inspected by ultrasonic testing without disassembly.
Furthermore, as a defect inspection device, for example, a device has been disclosed that inputs heat into the steel frame of reinforced concrete, measures the temperature distribution on the surface of the concrete with a thermal camera, and estimates internal defects in the steel frame and concrete using three-dimensional thermal analysis and inverse analysis (for example, Patent Document 1).

JP 2003-139731 A

When applying the device described in the patent document, it is difficult to fix the inspection device and heater inside the generator, and the heat flux distribution becomes unstable, which reduces the accuracy of defect determination.

This application discloses technology to solve the problems described above, and aims to realize a compact inspection device that can accurately estimate defects inside a structure even in environments where the heating of the inspection object becomes unstable.

The defect inspection device disclosed in the present application includes a first thermocamera that measures the time change of the surface temperature distribution in a first temperature measurement region of the inspection object, a second thermocamera that measures the time change of the surface temperature distribution in a second temperature measurement region of the inspection object, a heating unit that heats a part of the first temperature measurement region, a heat flux inverse analysis unit that estimates the heat flux by the heating unit using a three-dimensional thermal analysis finite element method model based on the measurement data of the first and second thermocameras, and a defect inverse analysis unit that estimates the position and size of a defect in the inspection object that is on a path along which heat is conducted from the first temperature measurement region to the second temperature measurement region of the inspection object using the heat flux estimation result of the heat flux inverse analysis unit, and an analysis unit that repeats the estimation of the heat flux by the heat flux inverse analysis unit and the subsequent estimation of the defect by the defect inverse analysis unit by updating the model of each analysis based on the result of the other, and determines the presence or absence of an abnormality in the inspection object from the estimated position and size of the defect in the inspection object.
The defect inspection device disclosed in the present application comprises a first thermocamera that measures a time change in a surface temperature distribution in a first temperature measurement region of an inspection object, a second thermocamera that measures a time change in a surface temperature distribution in a second temperature measurement region of the inspection object, a heating unit that heats a part of the first temperature measurement region, a heat flux inverse analysis unit that estimates a heat flux by the heating unit using a three-dimensional thermal analysis finite element method model based on measurement data from the first thermocamera, and a defect inverse analysis unit that estimates a position and a size of a defect in the inspection object that is on a path along which heat is conducted from the first temperature measurement region to the second temperature measurement region of the inspection object based on an estimation result of the heat flux inverse analysis unit and the measurement data of the second thermocamera, and an analysis unit that determines the presence or absence of an abnormality in the inspection object from the estimated position and size of the defect in the inspection object, and the analysis unit repeats an estimation of a heat flux by the heat flux inverse analysis unit and a subsequent estimation of the defect by the defect inverse analysis unit by updating a model of each analysis based on the result of the other.
The defect inspection device disclosed in the present application comprises a first thermo camera that measures a time change in a surface temperature distribution of a first temperature measurement region of an inspection object, a second thermo camera that measures a time change in the surface temperature distribution of a second temperature measurement region of the inspection object, a heating unit that heats a part of the first temperature measurement region, and an analysis unit that estimates a heat flux by the heating unit using a three-dimensional thermal analysis finite element method model based on measurement data of the first thermo camera and the second thermo camera, estimates a position and a size of a defect in the inspection object that is on a path along which heat is conducted from the first temperature measurement region to the second temperature measurement region of the inspection object using the estimated heat flux, and judges the presence or absence of an abnormality in the inspection object from the estimated position and size of the defect in the inspection object by repeating the above steps of estimating the heat flux again using the estimated result of the defect position and size. The analysis unit comprises a model generation unit, an estimation unit, and an analysis result judgment unit. The model generation unit creates a shape model based on the shape and material of the inspection object, and further judges whether or not an abnormality is present in the inspection object based on the estimated position and size of the defect in the inspection object. a heat flux estimation model and a defect estimation model are generated from the model, the estimation unit includes a heat flux inverse analysis unit that estimates a heat flux caused by the heating unit based on the heat flux estimation model generated by the model generation unit and the measurement data of the first thermo camera, and a defect inverse analysis unit that estimates a position and a size of a defect in the inspection object based on the defect estimation model generated by the model generation unit and the measurement data of the second thermo camera, the model generation unit updates a boundary condition of a heat flux of the heat flux estimation model based on an estimation result of the heat flux inverse analysis unit, and updates a thermal conductivity of an element at a position of the defect in the defect estimation model based on an estimation result of the defect inverse analysis unit, the model generation unit and the estimation unit cooperate to repeat the estimation of the heat flux by the heat flux inverse analysis unit, the estimation of the defect by the defect inverse analysis unit, the updating of the shape model by the model generation unit, and the modification of the heat flux estimation model and the defect estimation model based on the updating of the shape model, until an amount of change in the number of elements at the position of the defect becomes equal to or less than a reference value,
The analysis result determination unit determines the presence or absence of an abnormality in the inspection object from the size and position of the defect when the amount of change in the number of elements at the position of the defect becomes equal to or less than a reference value.

The defect inspection device disclosed in this application can accurately estimate defects inside a structure even in environments where the heating of the object to be inspected becomes unstable, and the defect inspection device can be made smaller.

1 is a functional block diagram of a defect inspection device according to a first embodiment. 2 is a layout diagram of an inspection object, a measurement unit, and a heating unit in the defect inspection device according to the first embodiment. 1 is an explanatory diagram of an analysis target of an inspection object in the defect inspection device according to the first embodiment; 4 is an explanatory diagram of a grid for analyzing a first temperature measurement region in the defect inspection device according to the first embodiment. FIG. 13 is an explanatory diagram of a grid for analyzing a second temperature measurement region in the defect inspection device according to the first embodiment. FIG. 4 is an explanatory diagram of a grid for analyzing a defect occurrence predicted region in the defect inspection apparatus according to the first embodiment; FIG. 4 is a flowchart of an inspection process performed by the defect inspection device according to the first embodiment. 1 is a block diagram of an example of a hardware configuration of a defect inspection device according to a first embodiment. 13 is a layout diagram of an inspection object, a measurement unit, and a heating unit in a defect inspection device according to a second embodiment. FIG.

Embodiment 1.
The first embodiment relates to a defect inspection device including first and second thermocameras that measure the change over time in the surface temperature distribution in first and second temperature measurement areas of an inspection object, a heating unit that heats a part of the first temperature measurement area, and an analysis unit that uses a three-dimensional thermal analysis finite element method model based on the measurement data of the first and second thermocameras to determine the heat flux from the heating unit and the position and size of defects in the inspection object that are on the thermal conduction path from the first temperature measurement area to the second temperature measurement area of the inspection object, and judges whether or not there is an abnormality in the inspection object.

The configuration and operation of the defect inspection device of embodiment 1 will be explained below based on Figure 1, which is a functional block diagram of the defect inspection device, Figure 2, which is a layout diagram of the inspection object, the measurement unit, and the heating unit, Figure 3, which is an explanatory diagram of the analysis target of the inspection object, Figure 4, which is an explanatory diagram of a grid for analyzing the first temperature measurement region, Figure 5, which is an explanatory diagram of a grid for analyzing the second temperature measurement region, Figure 6, which is an explanatory diagram of a grid for analyzing the region where defects are expected to occur, Figure 7, which is a flowchart of the inspection process, and Figure 8, which is a block diagram of an example hardware configuration.

The overall configuration of a defect inspection system and a defect inspection device 1 according to the first embodiment will be described with reference to the functional block diagram of FIG.
The defect inspection system is composed of a defect inspection device 1 and an inspection object 6 that is the target of defect inspection. The purpose of this defect inspection device 1 is to inspect the rotor wedge of a turbine generator for defects, but the entire rotor wedge of the turbine generator is omitted, and the object to be inspected using the three-dimensional thermal analysis finite element method is represented diagrammatically as the inspection object 6.

The configuration of the defect inspection device 1 will be described.
The defect inspection device 1 includes an analysis unit 2 , a data acquisition unit 3 , a measurement unit 4 , and a heating unit 5 .
The analysis unit 2 includes a model generation unit 21, an estimation unit 22, a repetition determination unit 23, and an analysis result determination unit 24. In addition, in Fig. 1, the "repetition determination unit" is written as "repetition determination unit".
The model generation unit 21 includes a shape model generation unit 211, a heat flux estimation model generation unit 212, and a defect estimation model generation unit 213. The estimation unit 22 includes a heat flux inverse analysis unit 221 and a defect inverse analysis unit 222.
The data acquiring unit 3 includes a first thermal data acquiring unit 31 and a second thermal data acquiring unit 32 .
The configuration, function and operation of each part of the defect inspection apparatus 1 will be described below in order of signal generation, measurement and processing, starting from the heating part 5 which is a heat source.

First, the configurations, functions and operations of the measurement unit 4 and the heating unit 5 will be described with reference to Fig. 2 which is a layout diagram of the inspection object 6, the measurement unit 4 and the heating unit 5. Additionally, the structure of the inspection object 6 will be described.
The measurement unit 4 includes two thermo cameras, that is, a first thermo camera 41 and a second thermo camera 42. The first thermo camera 41 and the second thermo camera 42 measure the temperatures of different areas of the surface of the inspection object 6.
The area measured by the first thermal camera 41 is referred to as a first temperature measurement area 61 , and the area measured by the second thermal camera 42 is referred to as a second temperature measurement area 62 .

The heating unit 5 is made up of a heater 51 and a power source (not shown). In Fig. 2, the heater 51 is brought into contact with a part of a first temperature measurement region 61 of the inspection object 6.
The heater 51 generates heat using power supplied from a power source, and when this heat is transmitted to a part of the first temperature measurement area 61, the temperature of the surface of the part of the first temperature measurement area 61 increases.

A thermal camera is equipped with a sensor that detects infrared rays and can simultaneously measure the surface temperature of an object over a wide area, thereby determining the temperature distribution.
The first thermo camera 41 and the second thermo camera 42 acquire the temperature distributions of the first temperature measurement area 61 and the second temperature measurement area 62 of the inspection object 6 at regular time intervals, respectively.

In general, infrared rays detected by a thermo camera include infrared rays generated by the temperature of the surface of the object being inspected, as well as infrared rays reflected from the surface of the object being inspected and reaching the thermo camera. By applying an anti-reflection paint or the like to the surface of the object being inspected 6, it is possible to suppress the infrared rays generated by the condition of the surface of the object being inspected 6, i.e., the infrared rays caused by reflection.
By suppressing the infrared rays reflected from the surface of the inspection object 6, it is possible to reduce errors in the temperature distribution measured by the first thermo camera 41 and the second thermo camera 42.

2, an area in which a defect is expected to occur in the inspection object 6 is defined as a defect occurrence expected area 63. The first temperature measurement area 61 and the second temperature measurement area 62 measured by the first thermo camera 41 and the second thermo camera 42 need to be disposed so as to sandwich the defect occurrence expected area 63. In other words, the defect in the inspection object 6 is on the path of heat conduction from the first temperature measurement area to the second temperature measurement area.
The defect occurrence predicted region 63 is determined by a separate structural analysis, as will be described later, and is set as data in the defect inspection device 1 .

It is necessary to heat a part of one of the first temperature measurement area 61 and the second temperature measurement area 62 by the heater 51 of the heating unit 5. In the first embodiment, the heater 51 is installed in the first temperature measurement area 61.
The first temperature measurement region 61 includes a region that is not heated by the heater 51. This is because the first thermal camera 41 measures the temperature distribution in the vicinity of the region heated by the heater 51.
The position and size of the area heated by the heater 51, i.e., the area in the first temperature measurement area 61 that is in contact with the heater 51, are calculated by the measurement unit 4 from the measurement data from the first thermo camera 41, and transmitted to the shape model generation unit 211 of the model generation unit 21. This transmission of information is indicated as A1 in FIG.

The first thermo camera 41 and the second thermo camera 42 of the measurement unit 4 measure the temperature distributions on the surfaces of the first temperature measurement area 61 and the second temperature measurement area 62, respectively, at regular time intervals. The measurement data of the measured temperature distributions is transmitted to the data acquisition unit 3.
The temperature distribution measured at regular time intervals in the first temperature measurement area 61 by the first thermo camera 41 is transmitted to the first thermal data acquisition unit 31 of the data acquisition unit 3. This transmission of information is indicated as A2 in FIG.
The temperature distribution measured at regular time intervals in the second temperature measurement region 62 is transmitted to the second thermal data acquisition unit 32 of the data acquisition section 3. This transmission of information is indicated as A3 in FIG.

The data acquisition unit 3 creates a data structure to be used in an analysis to estimate the heating heat flux and defects of the inspection object 6 from the temperature distributions measured at regular time intervals by each of the first thermo camera 41 and the second thermo camera 42 transmitted from the measurement unit 4, and transmits the data to the analysis unit 2. The data structure will be explained later.

Next, examples of divisions of lattice elements of the first temperature measurement area 61, the second temperature measurement area 62, and the defect occurrence expected area 63 of the inspection object 6 required for using the three-dimensional thermal analysis finite element method will be described with reference to Figures 3 to 6.
3 is a diagram in which a first temperature measurement region 61 and a second temperature measurement region 62 including the region where the heater 51 is installed of the inspection target 6 are divided into lattice points. In FIG. 3, they are designated as region 611 and region 621, respectively. The region where the heater 51 is installed is designated as region 511.
The defect occurrence predicted region 63 is an area 631, but since it is difficult to divide it into lattice points on the drawing, it is drawn with oblique lines.

4 illustrates an example of the division of the grid points, i.e., the analysis grid, of the first temperature measurement region 61. FIG. 4 corresponds to the region 611 in FIG.
4, the first temperature measurement area 61 on the surface of the inspection target 6 is divided into a plurality of lattices (elements A). Each element A is divided into m elements along the X-axis direction and n elements along the Y-axis direction. By dividing the first temperature measurement area 61 into m horizontal and n vertical lattices, the lattices intersect with each other, and m×n elements A are created in the first temperature measurement area 61.

The coordinates of each lattice point in the first temperature measurement area 61 are represented by (i, j). The origin of (i, j) is (0, 0). The position where the values of i and j are maximum is (m, n). If each lattice is a node, each node is a point located on the line that forms element A.

5 illustrates an example of the division of the grid points, i.e., the analysis grid, of the second temperature measurement region 62. FIG. 5 corresponds to the region 621 in FIG.
5, the second temperature measurement area 62 on the surface of the inspection target 6 is divided into a plurality of lattices (elements B). Each element B is divided into r elements along the X-axis direction and into s elements along the Y-axis direction. By dividing the second temperature measurement area 62 into r horizontal and s vertical lattices, the lattices intersect, and r×s elements B are created in the second temperature measurement area 62.
The coordinates of each lattice point in the second temperature measurement region 62 are represented by (i, j). The origin of (i, j) is (0, 0). The position where the values of i and j are maximum is (r, s). If each lattice is a node, each node is a point located on a line forming element B.

The data acquisition unit 3 calculates the temperature at each lattice point from the temperature distribution measured by the first thermo camera 41 and the second thermo camera 42 transmitted from the measurement unit 4. The number of lattice points created from the temperature distribution of the first thermo camera 41 and the second thermo camera 42 does not have to be the same.

An example of the division of lattice points, that is, lattice elements, of the defect occurrence predicted region 63 will now be described with reference to Fig. 6. Fig. 6 corresponds to the region 631 in Fig. 3.
6, the defect occurrence region 63 is divided into a plurality of elements C. Each element C is divided into j elements along the X-axis direction, k elements along the Z-axis direction, and one element along the Y-axis direction. That is, the element C of the defect occurrence region 63 is a hexahedron having a certain length in the Y-axis direction as well.

The coordinates of each lattice are represented by (i, j). The origin of (i, j) is (0, 0). The position where the values of i and j are maximum is (j, k). If each lattice point is considered as a node, each node is a point located on a line that forms element C.
The defect occurrence predicted region 63 contains j×k divided elements. The position of the element is represented by the lattice point with the smallest values of i and j among the four lattice points constituting the element. For example, the position of an element consisting of four lattice points (i, j), (i, j+1), (i+1, j), and (i+1, j+1) is represented as (i, j).

Next, the data structure used for the three-dimensional thermal analysis created by the data acquisition unit 3 will be described.
The time t when a certain temperature distribution is measured is expressed by the following formula (1), where the time when the measurement starts is 0 and the time interval of the measurement is Δt. Here, since the time interval for measuring the temperature distribution is constant, Δt is a constant value.

In formula (1), τ is an integer, and τ at the shooting end time is set to τmax.
The time to start and end temperature measurement of the first thermo camera 41 and the second thermo camera 42, and the time interval for measuring the temperature are set to the same values.
By setting in this way, the data of the distribution of temperatures measured by the first thermo camera 41 and the second thermo camera 42 at the same time can be organized by τ, which makes it possible to simplify and speed up the analysis of defects.

First, a description will be given of the data structure relating to the time change in temperature distribution in the first temperature measurement region 61. In the coordinate system of Fig. 4, the temperature at (i, j) at time t is defined as T1τ(i, j).
A surface temperature distribution vector T1τ representing the surface temperature distribution at time t is defined by equation (2). Since the number of lattice points in the first temperature measurement area 61 is (m+1)×(n+1), T1τ is an (m+1)×(n+1)-dimensional vector.

From this T1τ, the matrix T1 showing the change in temperature distribution on the surface over time is defined by equation (3) and is called the temperature distribution time matrix.

Similar to the creation of T1τ and T1 above, a surface temperature distribution vector T2τ and a temperature distribution time matrix T2 of the second temperature measurement area 62 are created from the measurement data of the second temperature measurement area 62 by the second thermo camera 42. However, T2τ is a (r+1)×(s+1) dimensional vector.
In the following description, there is no need to distinguish between the surface temperature distribution vector T1τ, the temperature distribution time matrix T1, and the surface temperature distribution vector T2τ, and the temperature distribution time matrix T2. When describing them together, they will be referred to as the surface temperature distribution vector Tτ and the temperature distribution time matrix T as appropriate.

The data acquisition unit 3 generates this temperature distribution time matrix T from the temperature distribution of each temperature measurement area measured at regular time intervals by two thermo cameras, and transmits it to the estimation unit 22 of the analysis unit 2.
The measurement data of the first temperature measurement region 61 measured by the first thermo camera 41 is transmitted to the first thermal data acquisition unit 31, which creates a temperature distribution time matrix T1. The first thermal data acquisition unit 31 transmits this temperature distribution time matrix T1 to the heat flux inverse analysis unit 221 of the estimation unit 22. In FIG. 1, the transmission of this information is indicated as C1.
The measurement data of the second temperature measurement region 62 measured by the second thermo camera 42 is transmitted to the second thermal data acquisition unit 32, which creates a temperature distribution time matrix T2. The second thermal data acquisition unit 32 transmits this temperature distribution time matrix T2 to the defect inverse analysis unit 222 of the estimation unit 22. In FIG. 1, the transmission of this information is indicated as C2.

Next, we will explain the functions and analysis process of the model generation unit 21, estimation unit 22, repetition judgment unit 23, and analysis result judgment unit 24 of the analysis unit 2.

The heat flux inverse analysis unit 221 of the estimation unit 22 estimates the heat flux from the heater 51 of the inspection object 6 based on the temperature distribution time matrix T1 transmitted from the data acquisition unit 3.
In addition, the defect inverse analysis unit 222 estimates the position and size of a defect in the defect occurrence predicted region 63 of the inspection object 6 based on the temperature distribution time matrix T2 transmitted from the data acquisition unit 3.
The analysis result determination unit 24 determines the distribution (positions and sizes) of defects in the inspection object 6 estimated by the defect inverse analysis unit 222 of the estimation unit 22, and outputs the result.

Next, the model generating unit 21 of the analysis unit 2 will be described.
A shape model generating section 211 of the model generating section 21 generates a shape model of the inspection object 6 from data on the shape and material of the inspection object 6 that are preset in the defect inspection device.
The shape model generation unit 211 transmits the generated shape model to the heat flux estimation model generation unit 212 and the defect estimation model generation unit 213. In Fig. 1, this information transmission is denoted as B1 and B2, respectively.

The heat flux estimation model generation unit 212 and the defect estimation model generation unit 213 each generate a three-dimensional thermal analysis model suitable for the respective purpose from the geometric model generated by the geometric model generation unit 211 .
The heat flux estimation model generating unit 212 generates a heat flux estimation model from the shape model and transmits it to the heat flux inverse analysis unit 221. In Fig. 1, this information transmission is indicated as D1.
The defect estimation model generating unit 213 generates a defect estimation model from the shape model and transmits it to the defect reverse analysis unit 222. In Fig. 1, this information transmission is indicated as D2.

The three-dimensional thermal analysis model is a model used when performing a three-dimensional thermal analysis. In order to perform a three-dimensional thermal analysis, a three-dimensional thermal analysis model and boundary conditions for the three-dimensional thermal analysis model are required.
The boundary conditions are composed of a heat flux and a fixed temperature boundary condition. That is, in order to perform a three-dimensional thermal analysis, three conditions are required: a three-dimensional thermal analysis model, a heat flux boundary condition, and a fixed temperature boundary condition. However, in the defect inspection device 1 of the first embodiment, the heat flux boundary condition by the heater 51 of the heating unit 5 is obtained by the inverse analysis process of the heat flux inverse analysis unit 221 of the estimation unit 22.

The boundary condition of the heat flux in the first temperature measurement region 61 other than the region heated by the heater 51 is defined by the temperature difference between the inspection object 6 and an object in contact with the inspection object 6 .
On the other hand, the fixed temperature boundary condition defines information that, when the inspection object 6 is in contact with the heat bath, the temperature change on the surface in contact with the heat bath is zero.
In the defect inspection device 1 of embodiment 1, the heat flux boundary conditions in areas other than the area heated by the heater 51 are set to adiabatic conditions, and analysis is performed assuming that the heat flow rate passing through the surface of the inspection object 6 other than the area heated by the heater 51 is zero.

The arrangement of lattice points and elements of the shape model generated by the shape model generation unit 211 is the same as the arrangement of lattice points created by the data acquisition unit 3. With this configuration, when performing analysis in the estimation unit 22, it becomes easier to apply the temperature distribution time matrices T1 and T2 transmitted from the data acquisition unit 3, and the analysis of heat flux and defects can be simplified.

The shape model generated by the shape model generating unit 211 will be described with reference to FIG.
In the shape model, a first temperature measurement area 61, a second temperature measurement area 62, an area heated by the heater 51 in the first temperature measurement area 61, and a defect occurrence predicted area 63 are set.
When the shape model generation unit 211 sets the first temperature measurement area 61 in the shape model and the area heated by the heater 51, the setting is based on the image measured by the first thermal camera 41 which measures the temperature distribution in the first temperature measurement area 61.
Furthermore, when the shape model generating unit 211 sets the second temperature measurement area 62 in the shape model, it sets it based on an image measured by a second thermo camera that measures the temperature distribution in the second temperature measurement area 62 .

The temperature of the area heated by the heater 51 cannot be measured because it is hidden by the heater 51 during heating. Therefore, the amount of heat flux passing through each element A in the surface area heated by the heater 51 is estimated by the inverse analysis process of the heat flux inverse analysis unit 221 of the analysis unit 2.
The heat flux distribution in the region heated by the heater 51 estimated by the heat flux inverse analysis part 221 of the analysis part 2 is transmitted to the shape model generation part 211. This transmission of information is indicated as E1 in FIG.

Here, the data structure of the heat flux in the region heated by the heater 51, which is estimated by the heat flux inverse analysis unit 221 of the estimation unit 22, will be described.
4, m contacts are arranged in the X direction and n contacts are arranged in the Y direction in the first temperature measurement region 61. Therefore, there are elements A divided into m×n elements on the surface.
Of these elements, an area that is directly heated by the heater 51 is determined based on the image measured by the first thermo camera 41. The elements of this area are set as an area on the model that is heated by the heater 51, and the heat flux that passes through each element and is transmitted from the heater 51 to the inspection target 6 is estimated by the heat flux inverse analysis unit 221 of the estimation unit 22.

The heat flux is calculated for each element. Of the four lattice points that make up an element, the position of that element is represented by the lattice point with the smallest values of i and j. For example, the position of an element consisting of the four lattice points (i,j), (i,j+1), (i+1,j), and (i+1,j+1) is represented as (i,j).

Let qτ(i,j) be the heat flux passing through an element at position (i,j) at a certain time t. As explained in equation (1), τ is an integer expressed as τ=t/Δt, where Δt is the time interval for measuring the temperature distribution and t=0 is the time when the measurement of the temperature distribution starts.
A heat flux distribution vector qτ representing the heat flux distribution in the region heated by the heater 51 at time t is defined by equation (4).

From this heat flux distribution vector qτ, the matrix q indicating the change over time in the heat flux distribution in the area heated by the heater 51 is defined by equation (5) and is called the heat flux distribution time matrix.

The heat flux inverse analysis unit 221 of the analysis unit 2 estimates this heat flux distribution time matrix q from the heat flux estimation model generated by the heat flux estimation model generation unit 212 and the temperature distribution time matrix T1 transmitted from the data acquisition unit 3. Then, the heat flux inverse analysis unit 221 transmits the heat flux distribution time matrix q to the shape model generation unit 211 of the model generation unit 21.

Next, updating of the shape model of the defect occurrence predicted region 63 in FIG. 6 based on the defect estimation result will be described.
As explained above, within the defect occurrence predicted region 63, there exists an element C divided into j × k pieces, and among the four lattice points constituting element C, the lattice point with the smallest values of i and j represents the position of the element.
Let z(i,j) represent whether the element at position (i,j) is a defective area. If the element at (i,j) is included in the defective area, z(i,j)=0, otherwise z(i,j)=1.
A defect distribution vector z that indicates the distribution of defects in the defect occurrence predicted region 63 is defined by equation (6).

The defect distribution vector z is estimated by inverse analysis by the defect inverse analysis unit 222 of the analysis unit 2, and is transmitted to the shape model generation unit 211. This transmission of information is indicated as E2 in Figure 1. However, this information is not transmitted directly from the defect inverse analysis unit 222 to the shape model generation unit 211, but rather passes through the iterative judgment unit 23.

In the shape model generating unit 211, the thermal conductivity of all elements is set to the thermal conductivity corresponding to the material of the inspection object 6.
The shape model generating unit 211 changes the thermal conductivity of the elements in the defect occurrence predicted region 63 based on the defect distribution vector z transmitted from the defect inverse analysis unit 222 of the estimation unit 22 .
Specifically, the thermal conductivity of an element estimated to be defective by the defect inverse analysis unit 222 is made lower than that of other elements, and the thermal conductivity of an element estimated not to be defective is made to correspond to the material of the object to be inspected. By configuring in this way, it is possible to generate a defective area in the shape model without changing the arrangement of the lattice points and elements of the shape model. This simplifies and speeds up the analysis of defects.

Here, the relationship between the model generating unit 21 and the estimating unit 22 will be summarized.
The heat flux inverse analysis unit 221 of the estimation unit 22 acquires a temperature distribution time matrix T1, which is time change data of the temperature distribution of the first temperature measurement region 61 including the region heated by the heater 51 measured by the first thermo camera 41, from the data acquisition unit 3. The heat flux inverse analysis unit 221 estimates the heat flux by the heater 51 using the heat flux estimation model generated by the heat flux estimation model generation unit 212 and the temperature distribution time matrix T1 from the data acquisition unit 3, and creates a heat flux distribution time matrix q. The heat flux inverse analysis unit 221 transmits this heat flux distribution time matrix q to the shape model generation unit 211.
The shape model generating unit 211 receives the heat flux distribution time matrix q and updates the shape model.

The defect inverse analysis unit 222 of the estimation unit 22 acquires a temperature distribution time matrix T2, which is time change data of the temperature distribution in the second temperature measurement region 62 measured by the second thermo camera 42, from the data acquisition unit 3. The defect inverse analysis unit 222 estimates the distribution of defects in the defect occurrence predicted region 63 using the defect estimation model generated by the defect estimation model generation unit 213 and the temperature distribution time matrix T2 from the data acquisition unit 3, and creates a defect distribution vector z. The defect inverse analysis unit 222 transmits this defect distribution vector z to the shape model generation unit 211.
The shape model generating unit 211 receives the defect distribution vector z and updates the shape model.

The heat flux of the shape model of the inspection object 6 is updated based on the heat flux inverse analysis results described above, and the distribution of defects within the defect occurrence predicted region 63 of the shape model of the inspection object 6 is updated based on the defect inverse analysis results.
The heat flux inverse analysis, the defect inverse analysis, and the updating of the shape model of the inspection object 6 based on these analysis results are repeated until the change in the inverse analysis result of the defect distribution falls below a reference value.

Specifically, the iterative determination unit 23 receives the defect distribution vector z, which is the estimation result, from the defect inverse analysis unit 222, and if the amount of change in the defect distribution vector z is not equal to or less than a reference value, transmits the defect distribution vector z, which is the estimation result of the defect inverse analysis unit, to the shape model generation unit 211. This transmission of information is indicated as F1 in Fig. 1. As described above, this information is transmitted from the defect inverse analysis unit 222 to the iterative determination unit 23.
Furthermore, if the amount of change from the previous defect distribution vector z is equal to or less than the reference value, the repetition judgment unit 23 transmits the defect distribution to the analysis result judgment unit 24 and ends the repetition. This information transmission is indicated as F2 in FIG.

An example of setting a reference value for terminating the repetition by the repetition determining unit 23 will be described.
When the defect distribution stops changing due to repeated defect inverse analysis, the reference value can be set to 0. When a value larger than 0 is used as the reference value, the determination can be made based on the difference in the amount of change in the defect distribution in the previous iteration.

The analysis result determination unit 24 calculates the fatigue life based on the location and size of the defect estimated by the defect inverse analysis unit 222, using the position and shape of the defect (crack), the shape of the inspection object 6, and data on the usage conditions provided to the defect inspection device 1. The analysis result determination unit 24 determines the need to replace the inspection object 6 based on the calculated fatigue life, and displays the result on a display device (not shown) of the defect inspection device 1 or outputs it externally.

In order to make it easier to understand the functions and operations of each part of the defect inspection apparatus 1 according to the first embodiment described above, the flow of a defect inspection process will be described with reference to the flowchart of FIG.
The defect inspection process of the first embodiment is made up of the following step 1 (S01) to step 9 (S09).

In the shape model generation step of step 1 (S01), the shape model generation unit 211 generates a shape model of the object to be inspected 6, including the area heated by the heater 51, based on the shape and material of the object to be inspected 6 and the images measured by the first thermal camera 41 and the second thermal camera 42.

In the heat flux estimation model generation step of step 2 (S02), the heat flux estimation model generation unit 212 generates a heat flux estimation model based on the shape model generated in step 1 (S01).

In the heating region heat flux estimation step of step 3 (S03), the heat flux inverse analysis unit 221 estimates the heat flux distribution time matrix q of the region heated by the heater 51 by inverse analysis based on the heat flux estimation model generated in step 2 (S02) and the temperature distribution time matrix T1 from the data acquisition unit 3.

In the first shape model update step of step 4 (S04), the shape model generation unit 211 updates the shape model of the object to be inspected 6, including the area heated by the heater 51, based on the heat flux distribution time matrix q estimated in step 3.

In the defect model generation step of step 5 (S05), the defect estimation model generation unit 213 generates a defect estimation model based on the shape model updated in step 4 (S04).

In the defect estimation step of step 6 (S06), the defect inverse analysis unit 222 estimates the distribution of defects in the defect occurrence predicted area 63 by inverse analysis based on the defect estimation model generated in step 5 (S05) and the temperature distribution time matrix T2 from the data acquisition unit 3.

In the repetition judgment step of step 7 (S07), the repetition judgment unit 23 finds the amount of change in the defect estimation result from the previous time, and judges whether the amount of change is equal to or less than a reference value. If the amount of change is equal to or less than the reference value, proceed to step 9 (S09). If the amount of change is greater than the reference value, proceed to step 8 (S08). If this is the first reverse analysis of the defect, proceed to step 8 (S08) without performing the judgment of step 7 (S07).

In the second shape model update step of step 8 (S08), the shape model generation unit 211 updates the shape model of the inspection object 6 based on the distribution of defects in the defect occurrence predicted region 63 obtained in step 6 (S06). After updating the shape model of the inspection object 6, the process returns to step 02 (S02).

In the anomaly determination step of step 9 (S09), the analysis result determination unit 24 determines the presence or absence of defects in the inspection object 6 from the distribution of defects (location and size) in the defect occurrence predicted area 63 analyzed in step 6 (S06). The result of the determination is then output.

As described above, in the first embodiment, the defect inspection device 1 includes the analysis unit 2 , the data acquisition unit 3 , the measurement unit 4 , and the heating unit 5 .
The heating unit 5 heats the surface of the inspection object 6. The measurement unit 4 measures the time change in temperature distribution in a first temperature measurement area 61 and a second temperature measurement area 62 of the inspection object 6. The data acquisition unit 3 creates temperature distribution time matrices T1 and T2 based on the time change data of the temperature distribution measured by the measurement unit 4.
The model generation unit 21 of the analysis unit 2 generates a shape model of the inspection object 6, and generates a heat flux estimation model and a defect estimation model from this shape model. The estimation unit 22 of the analysis unit 2 estimates the heat flux transmitted to the inspection object 6 by the heating unit 5, and estimates defects in the inspection object 6. Next, the model generation unit 21 of the analysis unit 2 updates the shape model based on the results of the heat flux estimation and defect estimation performed by the estimation unit 22. The analysis unit 2 repeats the heat flux estimation, defect estimation, and shape model update until the amount of change in the defect estimation result falls below a reference value. When the amount of change in the defect estimation result falls below a reference value, the defect estimation result is judged, and the judgment result is output.

By configuring in this manner, defects in the object to be inspected 6 and the heat flux from the heating section 5 can be estimated from the change over time in the temperature distribution on the surface of the object to be inspected 6, thereby improving the accuracy of defect estimation.

In addition, by estimating the heat flux and the defects separately from the time change in the temperature distribution on the surface of the inspection object 6, rather than simultaneously, the creation of the estimation model can be simplified. As a result, the estimation of the defects can be simplified and accelerated compared to the case where the heat flux and the defects are estimated simultaneously.

Measurement of the temperature distribution by the first thermo camera 41 and the second thermo camera 42 of the measurement unit 4 begins simultaneously with heating by the heating unit 5. Measurement by the first thermo camera 41 and the second thermo camera 42 of the measurement unit 4 continues while the inspection object 6 is being heated by the heating unit 5 and for a certain period of time after heating has ended. By configuring in this manner, it is possible to measure the thermal transient phenomenon caused by heating, and the time it takes to heat the inspection object 6 with the heater 51 can be shortened.

By measuring the temperature distribution on the surface of the inspection object 6 at regular time intervals, the thermal transient phenomenon caused by heating with the heater 51 is measured. With this configuration, it is not necessary to heat the inspection object 6 until it reaches thermal equilibrium, thereby shortening the inspection time.

The defect occurrence predicted region 63 of the shape model generation unit 211 is determined by structural analysis based on knowledge of material mechanics and fracture mechanics from the structure of the inspection object 6. By configuring in this way, it is possible to narrow the defect estimation region, simplify the analysis process for estimating the defect, and shorten the processing time.

The relative positions of the heating unit 5 of the defect inspection device 1 and the first thermo camera of the measurement unit 4 are fixed. As a result, the relative positions of the area heated by the heater 51 and the area measured by the first thermo camera 41 are fixed, making measurement easier.

Measurements of the first temperature measurement area 61 and the second temperature measurement area 62 may be performed with a single thermo camera. This configuration simplifies the configuration of the defect inspection device 1.

2 shows an example of a heater that comes into direct contact with the object to be inspected as the heater 51 of the heating unit 5. However, in addition to devices that come into direct contact, devices that heat the object to be inspected 6 without coming into contact with it may also be used, such as devices that heat the object surface with radiant heat, such as halogen heaters, devices that heat the object surface with a laser, and devices that heat the object surface with electromagnetic induction.

The time change in the distribution of a physical quantity on the surface of the test object 6 to be measured may be something other than heat. For example, it is possible to use the time change in the strain distribution when hammering is applied, or the temperature distribution and strain distribution when a thermal shock is applied.

An example of hardware of the defect inspection device 1 is shown in Fig. 8. As shown in Fig. 8, the device is composed of a processor 1000 and a storage device 1001. Although the storage device is not shown, it includes a volatile storage device such as a random access memory, and a non-volatile auxiliary storage device such as a flash memory.
Also, a hard disk auxiliary storage device may be provided instead of a flash memory. The processor 1000 executes a program input from the storage device 1001. In this case, the program is input from the auxiliary storage device to the processor 1000 via a volatile storage device. The processor 1000 may output data such as a calculation result to a volatile storage device of the storage device 1001, or may store data in the auxiliary storage device via the volatile storage device.

As described above, the defect inspection apparatus of embodiment 1 includes first and second thermocameras that measure the change over time in the surface temperature distribution in the first and second temperature measurement areas of the object to be inspected, a heating unit that heats a part of the first temperature measurement area, and an analysis unit that uses a three-dimensional thermal analysis finite element method model based on the measurement data of the first and second thermocameras to determine the heat flux from the heating unit and the position and size of defects in the object to be inspected that are on the thermal conduction path from the first temperature measurement area to the second temperature measurement area of the object to be inspected, and determines whether or not there is an abnormality in the object to be inspected.
Therefore, the defect inspection device of the first embodiment can accurately estimate defects inside a structure even in an environment where heating of the inspection object becomes unstable, and the device can be made compact.

Embodiment 2.
The defect inspection apparatus of the second embodiment inspects an object having a structure in which two or more parts are assembled.

The defect inspection device of the second embodiment will be described with reference to FIG. 9 which shows the layout of the inspection object, the measuring unit, and the heating unit, focusing on the differences from the first embodiment.
In FIG. 9 of the second embodiment, parts that are the same as or equivalent to those in the first embodiment are given the same reference numerals.
In order to distinguish from the first embodiment, there are an inspection object 7, a first temperature measurement area 71, a second temperature measurement area 72, and a defect occurrence predicted area 73.

9, an area in which a defect is expected to occur in the inspection object 7 is defined as a defect occurrence expected area 73. A first temperature measurement area 71 and a second temperature measurement area 72 measured by the first thermo camera 41 and the second thermo camera 42 are disposed so as to sandwich the defect occurrence expected area 73 therebetween.
The defect occurrence predicted region 73 is determined by a separate structural analysis and set as data in the defect inspection device 1 .

In the second embodiment, as shown in Fig. 9, a model in which two or more parts are assembled is generated by the model generation unit 21. In the model generated by the model generation unit 21, the first temperature measurement area 71 and the second temperature measurement area 72 are provided on the surfaces of different parts.

When the inspection target 7 has a structure in which two or more parts are assembled, the contact thermal resistance of the contact surface between the two parts becomes an issue. In the second embodiment, the surface pressure of the contact surface between the parts is calculated by structural analysis taking into account the shape and material of each part and the shape of the defect, and this surface pressure of the contact surface is set as data in the defect inspection device.
The shape model generation unit 211 calculates the contact thermal resistance of the surfaces where the components are in contact from the surface pressure of the contact surfaces of the components, and transmits this together with the shape model of the object to be inspected 7 to the heat flux estimation model generation unit 212 and the defect estimation model generation unit 213.

The heat flux estimation model generating unit 212 generates a heat flux estimation model using the shape model of the inspection object 7 transmitted from the shape model generating unit 211 and the contact thermal resistance of the contact surface of the component.
The defect estimation model generating unit 213 generates a defect estimation model using the shape model of the inspection object 7 transmitted from the shape model generating unit 211 and the thermal contact resistance of the contact surface of the component.

Other than those described above, the functions and processing of the analysis unit 2, data acquisition unit 3, measurement unit 4, and heating unit 5 in embodiment 2 are the same as those in embodiment 1.

The defect inspection device of embodiment 2 can estimate internal defects in an inspection object 7 consisting of multiple parts, and can improve the accuracy of defect estimation by thermal analysis that takes into account the surface pressure (thermal contact resistance) between the parts.

Examples of objects to be inspected using the defect inspection device of embodiment 2 include rotor wedges of a turbine generator and turbine blades of an aircraft.

As described above, the defect inspection apparatus of the second embodiment is adapted to inspect an object having a structure in which two or more parts are assembled.
Therefore, the defect inspection device of the second embodiment can accurately estimate defects inside a structure even in an environment where the heating of the inspection target becomes unstable, and the device can be made compact. Furthermore, the defect inspection device of the second embodiment can improve the accuracy of defect estimation by taking into account the contact thermal resistance between components.

Although the present application describes various exemplary embodiments and examples, the various features, aspects, and functions described in one or more embodiments are not limited to application to a particular embodiment, but may be applied to the embodiments alone or in various combinations.
Therefore, countless modifications not illustrated are conceivable within the scope of the technology disclosed in the present application, including, for example, modifying, adding, or omitting at least one component, and further, extracting at least one component and combining it with a component of another embodiment.

1 Defect inspection device, 2 Analysis unit, 3 Data acquisition unit, 4 Measurement unit, 5 Heating unit, 6, 7 Inspection object, 21 Model generation unit, 22 Estimation unit, 23 Repetitive judgment unit, 24 Analysis result judgment unit, 31 First thermal data acquisition unit, 32 Second thermal data acquisition unit, 41 First thermo camera, 42 Second thermo camera, 51 Heater, 61, 71 First temperature measurement area, 62, 72 Second temperature measurement area, 63, 73 Defect occurrence predicted area, 211 Shape model generation unit, 212 Heat flux estimation model generation unit, 213 Defect estimation model generation unit, 221 Heat flux inverse analysis unit, 222 Defect inverse analysis unit, 1000 Processor, 1001 Storage device, 511, 611, 621, 631 Area.

Claims (11)

A defect inspection device comprising: a first thermo camera that measures the time change of the surface temperature distribution in a first temperature measurement area of an inspection object; a second thermo camera that measures the time change of the surface temperature distribution in a second temperature measurement area of the inspection object; a heating unit that heats a part of the first temperature measurement area; a heat flux inverse analysis unit that estimates the heat flux by the heating unit using a three-dimensional thermal analysis finite element method model based on the measurement data of the first thermo camera and the second thermo camera; and a defect inverse analysis unit that estimates the position and size of a defect in the inspection object on a path along which heat is conducted from the first temperature measurement area to the second temperature measurement area of the inspection object using the estimation results of the heat flux inverse analysis unit, and an analysis unit that determines the presence or absence of an abnormality in the inspection object from the position and size of the defect in the inspection object estimated by repeatedly updating the model of each analysis based on the result of the other analysis and performing the estimation of the heat flux by the heat flux inverse analysis unit and the subsequent estimation of the defect by the defect inverse analysis unit. The defect inspection device according to claim 1, wherein the defect inverse analysis unit estimates the position and size of the defect in the inspection object from the time change in the temperature distribution of the second temperature measurement area. an analysis unit having a first thermo camera that measures a time change in a surface temperature distribution in a first temperature measurement area of an inspection object, a second thermo camera that measures a time change in a surface temperature distribution in a second temperature measurement area of the inspection object, a heating unit that heats a part of the first temperature measurement area, a heat flux inverse analysis unit that estimates a heat flux by the heating unit using a three-dimensional thermal analysis finite element method model based on measurement data of the first thermo camera, and a defect inverse analysis unit that estimates a position and a size of a defect in the inspection object that is on a path along which heat is conducted from the first temperature measurement area to the second temperature measurement area of the inspection object based on an estimation result of the heat flux inverse analysis unit and the measurement data of the second thermo camera, and the presence or absence of an abnormality in the inspection object is determined from the estimated position and size of the defect in the inspection object,
The analysis unit is a defect inspection device that repeats the estimation of heat flux by the heat flux inverse analysis unit and the subsequent estimation of the defect by the defect inverse analysis unit by updating the model of each analysis based on the result of the other analysis. the defect inverse analysis unit estimates a defect distribution vector representing a distribution of defects in the inspection object;
4. The defect inspection device according to claim 3, wherein the analysis unit repeats the process of estimating the heat flux by the heat flux inverse analysis unit and then estimating the defect by the defect inverse analysis unit until the change in the defect distribution vector from the previous defect estimation result becomes less than a reference value. the first thermo camera measuring the time change of the surface temperature distribution in a first temperature measurement area of the inspection object; a second thermo camera measuring the time change of the surface temperature distribution in a second temperature measurement area of the inspection object; a heating unit heating a part of the first temperature measurement area; and an analysis unit estimating a heat flux by the heating unit using a three-dimensional thermal analysis finite element method model based on measurement data of the first thermo camera and the second thermo camera, estimating a position and a size of a defect in the inspection object on a path along which heat is conducted from the first temperature measurement area to the second temperature measurement area of the inspection object using the estimated heat flux result, and repeating the process of estimating the heat flux again using the estimated position and size of the defect in the inspection object to determine the presence or absence of an abnormality in the inspection object from the estimated position and size of the defect in the inspection object,
the analysis unit includes a model generation unit, an estimation unit, and an analysis result determination unit;
the model generation unit creates a shape model based on a shape and a material of the inspection object, and further generates a heat flux estimation model and a defect estimation model from the shape model;
the estimation unit includes a heat flux inverse analysis unit that estimates a heat flux caused by the heating unit based on the heat flux estimation model generated by the model generation unit and measurement data of the first thermo camera, and a defect inverse analysis unit that estimates a position and a size of a defect in the inspection object based on the defect estimation model generated by the model generation unit and measurement data of the second thermo camera,
the model generation unit updates a boundary condition of a heat flux of the heat flux estimation model based on an estimation result of the heat flux inverse analysis unit, and updates a thermal conductivity of an element at a position of the defect of the defect estimation model based on an estimation result of the defect inverse analysis unit;
the model generation unit and the estimation unit cooperate to repeat the following steps: estimating the heat flux by the heat flux inverse analysis unit, estimating the defect by the defect inverse analysis unit, updating the shape model by the model generation unit, and modifying the heat flux estimation model and the defect estimation model based on the update of the shape model, until a change in the number of elements at the position of the defect becomes equal to or less than a reference value;
The analysis result determination unit is a defect inspection device that determines whether or not the object to be inspected has an abnormality based on the size and position of the defect when a change in the number of elements at the position of the defect falls below a reference value. the model generation unit includes a shape model generation unit, a heat flux estimation model generation unit, and a defect estimation model generation unit;
the shape model generation unit generates the shape model of the object to be inspected based on a preset shape and material of the object to be inspected;
the heat flux estimation model generation unit generates the heat flux estimation model from the shape model generated by the shape model generation unit;
The defect inspection device according to claim 5 , wherein the defect estimation model generating unit generates the defect estimation model from the shape model generated by the shape model generating unit. A defect inspection device as described in any one of claims 1 to 6, configured to measure the time change of the surface temperature distribution of the first temperature measurement area of the inspection object and the time change of the surface temperature distribution of the second temperature measurement area of the inspection object using a single thermal camera. 7. The defect inspection device according to claim 1, wherein a relative position between the heating unit and the first thermo camera is fixed. 7. The defect inspection device according to claim 5 or claim 6, configured to determine in advance a region in which a defect is expected to occur in the inspection object through a structural analysis of the structure of the inspection object, and to set in advance data of the region in which the defect is expected to occur in the model generation unit of the analysis unit. The inspection object is composed of a plurality of parts,
A surface pressure on the contact surfaces of the plurality of components due to a defect in the inspection object is obtained by a structural analysis, and the surface pressure on the contact surfaces is set in advance in the model generation unit of the analysis unit;
6. The defect inspection device according to claim 5, wherein the model generation unit of the analysis unit generates a shape model of the object to be inspected, calculates a contact thermal resistance from a surface pressure of the contact surface, and uses the calculated contact thermal resistance in generating the heat flux estimation model and the defect estimation model. The defect inspection device according to any one of claims 1 to 10, wherein the heating unit is a non-contact heating device which is either an equipment for heating the surface of the heating target with radiant heat , an equipment for heating the surface of the heating target with a laser, or an equipment for heating the surface of the heating target with electromagnetic induction.

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