Damage inspection method and damage inspection system

Abstract

To easily and accurately determine the degree of creep damage in an inspected component.SOLUTION: A damage inspection process includes: a detection step S0 for acquiring Barkhausen noise generated in an inspected component by application of a magnetic field generated in an excitation coil by supplying an excitation current; a first analysis step S1 for generating magnetic property data representing the characteristics of the Barkhausen noise; and a second analysis step S2 for generating damage factor data relating to the factors of creep damage generated in the inspected component from the magnetic property data.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to a technique for non-destructively inspecting an object for damage. [Background technology]

[0002] Techniques for non-destructively inspecting damage to various structures have been proposed. For example, Patent Document 1 discloses a configuration for detecting the degree of embrittlement in a test object by analyzing Barkhausen noise generated in the test object when a magnetic field is applied to the test object by an excitation coil. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2-78948 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology of Patent Document 1 is a technology for detecting the degree of embrittlement of a measurement object, but various types of damage, such as creep damage, can occur in actual structures. In consideration of the above circumstances, one aspect of the present disclosure aims to easily and accurately identify the degree of creep damage in an inspection target component. [Means for solving the problem]

[0005] In order to solve the above problems, a damage inspection method according to one embodiment of the present disclosure includes a detection step of acquiring Barkhausen noise generated in an inspected component by applying a magnetic field generated in an excitation coil by supplying an excitation current, a first analysis step of generating magnetic property data representing the characteristics of the Barkhausen noise, and a second analysis step of generating damage factor data relating to factors of creep damage generated in the inspected component from the magnetic property data.

[0006] A damage inspection system according to one aspect of the present disclosure includes an excitation device that applies a magnetic field generated in an excitation coil by supplying an excitation current to an inspection target component, and an inspection device that inspects the degree of creep damage in the inspection target component, wherein the inspection device acquires Barkhausen noise generated in the inspection target component, generates magnetic property data representing the characteristics of the Barkhausen noise, and generates damage factor data related to the factors of creep damage generated in the inspection target component from the magnetic property data. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a block diagram illustrating the configuration of a damage inspection system according to a first embodiment of the present disclosure. [Figure 2] 3A and 3B are schematic diagrams of excitation current and detection signals. [Figure 3] FIG. 10 is a schematic diagram of measurement noise characteristics. [Figure 4] 10 is a flowchart illustrating a specific procedure of a damage inspection process. [Figure 5] FIG. 10 is an explanatory diagram relating to a specific example of magnetic property data. [Figure 6] 1 is an example of the correlation between magnetic property data and damage factor data. [Figure 7] FIG. 10 is a schematic diagram showing the relationship between magnetic property data and damage factor data. [Figure 8] FIG. 10 is a schematic diagram of a look-up table used in the second analysis step. [Figure 9] 10 is a flowchart of a damage inspection process in the second embodiment. [Figure 10] FIG. 10 is a schematic diagram showing the relationship between damage factor data and remaining lifespan. [Figure 11] FIG. 10 is a schematic diagram of a look-up table used in the third analysis step. [Figure 12] FIG. 11 is an explanatory diagram relating to a specific example of magnetic property data in the third embodiment. [Figure 13] 10 is an example of the correlation between magnetic property data and damage factor data in the third embodiment. [Figure 14]FIG. 10 is an explanatory diagram relating to generation of magnetic characteristic data in a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0008] The embodiments for carrying out the present disclosure will be described with reference to the drawings. Note that the dimensions and scale of each element in each drawing may differ from those of the actual product. Furthermore, the embodiment described below is an exemplary embodiment that may be envisioned when carrying out the present disclosure. Therefore, the scope of the present disclosure is not limited to the embodiment exemplified below.

[0009] A: First embodiment 1 is a block diagram illustrating the configuration of a damage inspection system 100 according to the first embodiment. The damage inspection system 100 is a measuring device for non-destructively inspecting the degree of creep damage in an inspection target component 10.

[0010] The inspection target component 10 is a magnetizable structure made of a magnetic material such as a ferromagnetic material. The inspection target component 10 is assumed to be a steel material (e.g., a cast material or a forged material) manufactured by heat treatment such as quenching. The material of the inspection target component 10 is arbitrary, but an example is 1% CrMoV (chromium-molybdenum-vanadium) cast steel. The inspection target component 10 is, for example, a mechanical part such as a turbine casing used in power generation equipment such as a thermal power generation equipment. However, the use or structure of the inspection target component 10 is arbitrary within the scope of this disclosure.

[0011] 1, the damage inspection system 100 includes an excitation device 20 and an inspection device 30. The excitation device 20 is electrically connected to the inspection device 30 via, for example, a signal line. The excitation device 20 is an inspection probe that can be moved while held by a user, and generates a magnetic field (hereinafter referred to as an "external magnetic field") for magnetizing the inspection target component 10.

[0012] The inspection device 30 is a computer system that inspects the inspection target component 10 for creep damage based on the result of magnetizing the inspection target component 10 by the excitation device 20. The inspection device 30 of the first embodiment includes a current supply device 31, a signal processing device 32, and an information processing device 40. Although the current supply device 31, the signal processing device 32, and the information processing device 40 are illustrated as separate devices in FIG. 1, the current supply device 31, the signal processing device 32, and the information processing device 40 may be configured as a single device. The functions of one of the current supply device 31 and the signal processing device 32 may be installed in the information processing device 40.

[0013] The current supply device 31 is a power supply device for supplying the excitation current E to the exciter 20. The excitation current E is a periodic signal whose current value fluctuates at a predetermined period (for example, 1 Hz), as illustrated in Fig. 2. Specifically, the excitation current E is supplied to the exciter 20 as, for example, a triangular wave whose current value fluctuates within a predetermined range.

[0014] 1, the excitation device 20 includes an excitation coil 21, a yoke 22, and a detection coil 23. The yoke 22 is a substantially U-shaped yoke including a protruding portion 221, a protruding portion 222, and a base portion 223. The base portion 223 is a long portion that is held substantially parallel to the surface 11 of the inspection target component 10. The protruding portions 221 and 222 protrude from both ends of the base portion 223 toward the inspection target component 10. The excitation device 20 is installed so that the tips of the protruding portions 221 and 222 contact the surface 11 of the inspection target component 10.

[0015] The excitation coil 21 is a coil wound around the base portion 223. The number of turns of the excitation coil 21 is, for example, approximately 150. An external magnetic field is generated in the excitation coil 21 by supplying an excitation current E. The inspection target member 10 is magnetized by application of the external magnetic field generated in the excitation coil 21. As explained above, the excitation device 20 applies the external magnetic field generated in the excitation coil 21 by supplying the excitation current E to the inspection target member 10.

[0016] The detection coil 23 is a coil for detecting Barkhausen noise generated in the inspection target member 10 due to the application of an external magnetic field. Specifically, the detection coil 23 is an air-core coil installed in the space between the protrusions 221 and 222, and is in contact with the surface 11 of the inspection target member 10. The number of turns of the detection coil 23 is, for example, approximately 750 to 1000, which exceeds the number of turns of the excitation coil 21. A detection signal Q0 representing fluctuations in current generated in the detection coil 23 due to magnetization of the inspection target member 10 is supplied from the excitation device 20 to the signal processing device 32.

[0017] The signal processing device 32 generates a detection signal Q that represents Barkhausen noise generated in the test object member 10. Barkhausen noise is magnetic noise caused by discontinuous movement of domain walls within the test object member 10. The discontinuous movement of domain walls is affected by the degree of creep damage that has occurred inside the test object member 10. Specifically, the Barkhausen noise of the test object member 10 depends on various factors (causes) related to creep damage in the test object member 10, such as precipitates, creep voids, or dislocations inside the test object member 10. In other words, the various factors of creep damage present in the test object member 10 are reflected in the Barkhausen noise.

[0018] 2, the detection signal Q is a periodic signal whose signal level fluctuates periodically in synchronization with the excitation current E. The signal processing device 32 generates the detection signal Q by performing various signal processing on the detection signal Q0. The signal processing by the signal processing device 32 includes, for example, an A / D conversion process that converts the detection signal Q0 from analog to digital, an amplification process that amplifies the detection signal Q0, and a filter process that extracts frequency components including Barkhausen noise from the detection signal Q0. The detection signal Q generated by the signal processing device 32 is supplied to the information processing device 40.

[0019] The information processing device 40 in Fig. 1 inspects creep damage in the inspection target component 10 using the detection signal Q generated by the signal processing device 32. The information processing device 40 is realized by an information device such as a personal computer or a tablet terminal. Specifically, the information processing device 40 includes a control device 41, a storage device 42, a display device 43, and an operation device 44. The information processing device 40 may be realized by a single device, or may be realized by multiple devices configured separately from each other.

[0020] The control device 41 is configured with one or more processors that control each element of the information processing device 40. Specifically, the control device 41 is configured with one or more types of processors, such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), or an ASIC (Application Specific Integrated Circuit).

[0021] The storage device 42 is one or more memories that store programs executed by the control device 41 and data used by the control device 41. The storage device 42 is configured with a known recording medium, such as a magnetic recording medium or a semiconductor recording medium. The storage device 42 may also be configured with a combination of multiple types of recording media. Note that the control device 41 may execute a program stored in the storage device 42 to realize part or all of the signal processing by the signal processing device 32.

[0022] The display device 43 displays an image under the control of the control device 41. Specifically, the display device 43 displays the results of the creep damage inspection in the inspection target component 10. The operation device 44 is an input device that accepts operations from a user. Note that the display device 43 or the operation device 44, which are separate from the information processing device 40, may be connected to the information processing device 40 by wire or wirelessly.

[0023] 3 is an explanatory diagram of the measurement noise characteristic F. The measurement noise characteristic F is a curve that shows the relationship between the current value of the excitation current E supplied to the excitation device 20 to apply an external magnetic field to the inspection target component 10, and the intensity V of Barkhausen noise generated in the inspection target component 10 by the application of the external magnetic field. The intensity V is, for example, the signal intensity (effective voltage value) of the detection signal Q. The measurement noise characteristic F is generated by integrating the detection signal Q with respect to time.

[0024] FIG. 3 also shows the measurement noise characteristic F for each of a plurality of measurement samples 1 to 3, which differ in the degree of creep damage that occurred in the test object member 10. Each test object member 10 is a cylindrical body with a diameter of 15 mm made of 1% CrMoV cast steel, and was manufactured by high-frequency heat treatment at 1350°C followed by annealing for 8 hours. Specifically, measurement samples 1 to 3 were machined into square columns with sides of 2 cm after applying a load at the temperatures described below.

[0025] Measurement sample 1 was subjected to a load of 24 kgf continuously for 3,000 hours in a high-temperature environment of 500°C. Measurement sample 2 was subjected to a load of 15 kgf continuously for 5,000 hours in a high-temperature environment of 550°C. Measurement sample 3 was subjected to a load of 8 kgf continuously for 5,000 hours in a high-temperature environment of 600°C. Measurement sample 2 suffered creep damage to a greater extent than measurement sample 1, and measurement sample 3 suffered creep damage to a greater extent than measurement sample 2.

[0026] 3, the shape of the measurement noise characteristic F differs depending on the degree of creep damage that has occurred in the inspection target component 10. In other words, there is a correlation between the shape of the measurement noise characteristic F and the degree of creep damage in the inspection target component 10. In the first embodiment, the above correlation is utilized to inspect the degree of creep damage in the inspection target component 10 from the characteristics of the measurement noise characteristic F.

[0027] FIG. 4 is a flowchart of a process (hereinafter referred to as the "damage inspection process") in which the control device 41 of the information processing device 40 uses the detection signal Q to inspect the creep damage of the inspection target component 10. A user of the damage inspection system 100 instructs the information processing device 40 to start the damage inspection process by operating the operation device 44 while the inspection device 30 is in contact with the surface 11 of the inspection target component 10. The damage inspection process is started in response to an instruction from the user. The damage inspection process is an example of a "damage inspection method." In the following description, an example is given in which the damage inspection process is executed by the control device 41 of the information processing device 40, but the damage inspection process may also be thought of as an overall operating method of the inspection device 30.

[0028] When the damage inspection process is started, the control device 41 executes a detection step S0. In the detection step S0, the control device 41 acquires Barkhausen noise generated in the inspection target component 10 due to application of an external magnetic field. As illustrated in Fig. 4, the detection step S0 includes a power supply step S01 and an acquisition step S02.

[0029] In the power supply step S01, the control device 41 instructs the current supply device 31 to supply the excitation current E to the excitation device 20 (excitation coil 21). The current supply device 31 supplies the excitation current E to the excitation coil 21 in response to the instruction from the control device 41.

[0030] In the acquisition step S02, the control device 41 acquires the Barkhausen noise generated in the inspection target member 10 due to the supply of the excitation current E. Specifically, the control device 41 acquires a detection signal Q representing the Barkhausen noise generated in the inspection target member 10 from the signal processing device 32. Note that the signal processing performed by the signal processing device 32 to generate the detection signal Q may be included in the acquisition step S02. As described above, the control device 41 functions as an element that acquires the Barkhausen noise generated in the inspection target member 10 due to the application of an external magnetic field that is generated in the excitation coil 21 by the supply of the excitation current E.

[0031] 4, the control device 41 executes a first analysis step S1 after executing the detection step S0. In the first analysis step S1, the control device 41 generates magnetic characteristic data Dx that represents the characteristics of the Barkhausen noise. That is, the control device 41 functions as an element that generates the magnetic characteristic data Dx.

[0032] 4, the first analysis step S1 includes a first process S11 and a second process S12. In the first process S11, the control device 41 generates a measured noise characteristic F by integrating the detection signal Q over a predetermined range on the time axis. In the second process S12, the control device 41 generates magnetic characteristic data Dx that represents the characteristics of the measured noise characteristic F.

[0033] 5 is an explanatory diagram of a specific example of the magnetic characteristic data Dx. The magnetic characteristic data Dx includes any one of the following pieces of information (hereinafter referred to as "characteristic index Xm") related to the measured noise characteristic F (m=1, 2, 3, . . .).

[0034] (1) Area x1 As illustrated in FIG. 5, the area X1 of the measurement noise characteristic F is the area of ​​the region between the measurement noise characteristic F and the current axis representing the excitation current E (i.e., the horizontal axis of the measurement noise characteristic F). The area X1 can also be expressed as the area of ​​the peak present in the measurement noise characteristic F. As can be seen from FIG. 3, the area X1 of the measurement noise characteristic F changes depending on the degree of creep damage in the inspection target component 10. Specifically, there is a tendency for the area X1 of the measurement noise characteristic F to increase as the creep damage of the inspection target component 10 progresses.

[0035] (2) Peak value X2 As illustrated in Fig. 5, the peak value X2 of the measurement noise characteristic F is the intensity V of the Barkhausen noise at the peak observed in the measurement noise characteristic F (i.e., the maximum value of the measurement noise characteristic F). As can be seen from Fig. 3, the peak value X2 of the measurement noise characteristic F changes depending on the degree of creep damage in the inspected component 10. Specifically, there is a tendency for the peak value X2 of the measurement noise characteristic F to decrease as the creep damage of the inspected component 10 progresses.

[0036] (3) Peak current X3 As illustrated in FIG. 5, the peak current X3 of the measurement noise characteristic F is the current value (absolute value) of the excitation current E corresponding to the peak of the measurement noise characteristic F. The peak current X3 corresponds to the position of the peak in the measurement noise characteristic F. As can be seen from FIG. 3, the peak current X3 of the measurement noise characteristic F changes depending on the degree of creep damage in the inspection target component 10. Specifically, there is a tendency for the peak current X3 of the measurement noise characteristic F to increase as the creep damage of the inspection target component 10 progresses.

[0037] 4, the control device 41 executes a second analysis step S2 after executing a first analysis step S1. In the second analysis step S2, the control device 41 generates damage factor data Dy from the magnetic property data Dx generated in the first analysis step S1. That is, the control device 41 functions as an element that generates the damage factor data Dy.

[0038] The damage factor data Dy is data relating to factors of creep damage occurring in the inspected component 10. The damage factor data Dy includes any of the following multiple pieces of information (hereinafter referred to as "damage indicators Yn") that depend on creep damage in the inspected component 10 (n=1, 2, 3, ...). Note that the combination of the characteristic indicator Xm included in the magnetic characteristic data Dx and the damage indicator Yn included in the damage factor data Dy is arbitrary.

[0039] (1) Precipitation amount Y1 The amount of precipitation Y1 is an index of the degree (e.g., weight or volume) of precipitation of a substance that causes creep damage in the test object member 10. The amount of precipitation Y1 depends on the degree of creep damage. Specifically, it can be evaluated that the larger the amount of precipitation Y1, the more advanced the creep damage.

[0040] (2) Creep void generation rate Y2 The creep void generation rate Y2 is an index of the degree to which creep voids are generated in the test component 10. Creep voids are tiny cavities that are generated inside the test component 10 due to the continuous application of load in a high-temperature environment. For example, the number or size (e.g., area or length) of creep voids is exemplified as the creep void generation rate Y2. The creep void generation rate Y2 depends on the degree of creep damage. Specifically, it can be evaluated that the larger the creep void generation rate Y2, the more advanced the creep damage.

[0041] (3) Dislocation generation rate Y3 The dislocation generation rate Y3 is an index of the degree to which dislocations occur in the test object member 10. Dislocations are deformations caused by the sliding of crystal grains. The dislocation generation rate Y3 depends on the degree of creep damage. It can be evaluated that the larger the dislocation generation rate Y3, the more advanced the creep damage.

[0042] (4) Grain size Y4 The grain size Y4 is an index of the grain size of the grains in the test object member 10. The grain size Y4 depends on the degree of creep damage. Specifically, it can be evaluated that the smaller the grain size Y4, the more advanced the creep damage.

[0043] (5)Hardness Y5 The hardness Y5 is an index of the hardness of the test object member 10. The hardness Y5 depends on the degree of creep damage. Specifically, it can be evaluated that the lower the hardness Y5, the more advanced the creep damage.

[0044] As described above, there is a correlation between the shape of the measurement noise characteristic F and the degree of creep damage in the test object component 10. Therefore, there is a correlation between the magnetic property data Dx identified from the test object component 10 and the damage factor data Dy related to the test object component 10.

[0045] Fig. 6 shows an example of the correlation between magnetic property data Dx and damage factor data Dy. Fig. 6 illustrates the relationship between the amount Y1 of carbide (e.g., chromium carbide) precipitated in the test object 10 and the area X1 of the measurement noise characteristic F. As can be seen from Fig. 6, the area X1 of the measurement noise characteristic F tends to increase as the amount Y1 of precipitation increases (i.e., as creep damage progresses).

[0046] FIG. 7 is a schematic diagram of the relationship between magnetic property data Dx and damage factor data Dy. The relationship between the magnetic property data Dx and the damage factor data Dy is expressed by a calibration curve Cxy that is statistically or experimentally determined in advance. Specifically, for each of a large number of measurement samples, the magnetic property data Dx generated from the measurement noise characteristic F of the measurement sample and the damage factor data Dy generated by observing the measurement sample are aggregated, and the calibration curve Cxy is determined based on the aggregation results. Note that FIG. 7 conveniently illustrates a case where a positive correlation exists between the magnetic property data Dx and the damage factor data Dy, but a relationship exists between the magnetic property data Dx and the damage factor data Dy that corresponds to the type (X1 to X3) of the characteristic index Xm included in the magnetic property data Dx and the type (Y1 to Y5) of the damage index Yn included in the damage factor data Dy. For example, a negative correlation may also exist between the magnetic property data Dx and the damage factor data Dy.

[0047] The storage device 42 of the information processing device 40 stores the relationship between the magnetic property data Dx and the damage factor data Dy. Specifically, the relationship between the magnetic property data Dx and the damage factor data Dy (i.e., the calibration curve Cxy) is stored in the storage device 42 as a look-up table Txy shown in FIG. 8. The look-up table Txy is a data table in which correspondence between each of the multiple magnetic property data Dx (Dx1, Dx2, ...) and each of the multiple damage factor data Dy (Dy1, Dy2, ...) is registered. In the second analysis step S2, the control device 41 identifies the damage factor data Dy corresponding to the magnetic property data Dx generated in the first analysis step S1 from the multiple damage factor data Dy registered in the look-up table Txy.

[0048] 4, the control device 41 executes a result output step S4 after executing the second analysis step S2. In the result output step S4, the control device 41 displays the damage factor data Dy identified for the inspection target component 10 on the display device 43. Specifically, each of the damage indices Yn described above corresponding to the degree of creep damage in the inspection target component 10 is displayed on the display device 43.

[0049] 7, the magnetic property data Dx may change for each first analysis step S1. Therefore, the damage factor data Dy may also change for each second analysis step S2. In the result output step S4, the control device 41 may display on the display device 43 the range ΔY within which the damage factor data Dy is distributed.

[0050] The control device 41 determines whether a predetermined termination condition is met (S5). The termination condition is, for example, when an instruction to terminate the damage inspection process is issued via an operation on the operation device 44, or when a predetermined time has elapsed since the start of the damage inspection process. If the termination condition is met (S5: YES), the control device 41 terminates the damage inspection process. On the other hand, if the termination condition is not met (S5: NO), the control device 41 transitions the process to the detection step S0. That is, the detection step S0, the first analysis step S1, the second analysis step S2, and the result output step S4 are repeated until the termination condition is met.

[0051] The user moves the excitation device 20 to a desired position on the inspection target component 10 while checking the display on the display device 43. That is, the position of the excitation device 20 relative to the inspection target component 10 is changed as needed in parallel with the damage inspection process. Therefore, the degree of creep damage (damage factor data Dy) at each position on the inspection target component 10 is displayed on the display device 43 in chronological order.

[0052] As described above, in the first embodiment, damage factor data Dy related to creep damage in the test object 10 is determined from magnetic property data Dx that represents the characteristics of Barkhausen noise generated in the test object 10. Therefore, the degree of creep damage in the test object 10 can be determined easily and with high accuracy. For example, in the technology of Patent Document 1, the object to be measured needs to be maintained at a low temperature in advance. According to the first embodiment, the degree of creep damage in the test object 10 can be determined without requiring processing such as cooling the test object 10.

[0053] In particular, in the first embodiment, magnetic property data Dx is generated that represents the characteristics of the measurement noise characteristic F. Since the measurement noise characteristic F varies depending on the degree of creep damage in the test object member 10, by using the magnetic property data Dx that represents the characteristics of the measurement noise characteristic F, the degree of creep damage in the test object member 10 can be identified with high accuracy.

[0054] The area X1 of the measurement noise characteristic F in the test object component 10 varies depending on the degree of creep damage in the test object component 10. Therefore, by using the magnetic characteristic data Dx including the area X1 of the measurement noise characteristic F, the degree of creep damage in the test object component 10 can be determined with high accuracy.

[0055] The peak value X2 in the measured noise characteristic F of the test object component 10 varies depending on the degree of creep damage in the test object component 10. Therefore, by using the magnetic characteristic data Dx including the peak value X2 of the measured noise characteristic F, the degree of creep damage in the test object component 10 can be determined with high accuracy.

[0056] The peak current X3 in the measured noise characteristic F of the inspection target component 10 (i.e., the position of the peak in the measured noise characteristic F) varies depending on the degree of creep damage in the inspection target component 10. Therefore, by using the magnetic characteristic data Dx including the peak current X3 in the measured noise characteristic F, the degree of creep damage in the inspection target component 10 can be identified with high accuracy.

[0057] Furthermore, the creep damage factors (Y1, Y2, Y3, Y4, Y5) exemplified in the first embodiment are significantly correlated with the characteristics of Barkhausen noise. Therefore, according to the first embodiment, the degree of creep damage in the inspection target component 10 can be easily and accurately determined.

[0058] B: Second embodiment A second embodiment of the present disclosure will be described. Note that, for elements in the following exemplary aspects that have the same functions as those in the first embodiment, the same reference numerals as those in the first embodiment will be used, and detailed descriptions of each will be omitted as appropriate.

[0059] Fig. 9 is a flowchart of the damage inspection process in the second embodiment. As illustrated in Fig. 9, in the second embodiment, a third analysis step S3 is added to the damage inspection process similar to that in the first embodiment. The control device 41 executes the second analysis step S2 and then executes the third analysis step S3.

[0060] In the third analysis step S3, the control device 41 estimates the remaining life Z of the inspected component 10 from the damage factor data Dy generated in the second analysis step S2. The remaining life Z is the expected length of time for which the inspected component 10 can be safely used. Specifically, the remaining life Z is the length of time from the present time until creep damage in the inspected component 10 progresses to a specific level.

[0061] There is a correlation between the degree of creep damage in the inspection target component 10 (that is, the damage factor data Dy) and the remaining life Z of the inspection target component 10. The storage device 42 of the information processing device 40 stores the relationship between the damage factor data Dy and the remaining life Z.

[0062] FIG. 10 is a schematic diagram of the relationship between damage factor data Dy and remaining life Z. The relationship between the damage factor data Dy and remaining life Z is expressed by a calibration curve Cyz that is statistically or experimentally determined in advance. Specifically, for each of a large number of measurement samples, the damage factor data Dy of the measurement sample and the remaining life Z of the measurement sample are compiled, and the calibration curve Cyz is determined based on the compilation results. Note that while FIG. 10 conveniently illustrates a case where a positive correlation exists between the damage factor data Dy and remaining life Z, a relationship exists between the damage factor data Dy and remaining life Z depending on the type (Y1 to Y5) of the damage index Yn included in the damage factor data Dy. For example, a negative correlation may also exist between the damage factor data Dy and remaining life Z. Furthermore, the remaining life Z and the damage factor data Dy may be expressed using temperature and time as variables (Larson-Miller parameter).

[0063] The relationship between the damage factor data Dy and the remaining life Z (i.e., the calibration curve Cyz) is stored in the storage device 42 as a reference table Tyz of Fig. 11. The reference table Tyz is a data table in which correspondence between each of the multiple damage factor data Dy (Dy1, Dy2, ...) and each of the multiple remaining life Z (Z1, Z2, ...) is registered. In the third analysis step S3, the control device 41 identifies the remaining life Z that corresponds to the damage factor data Dy generated in the second analysis step S2 from among the multiple remaining life Z registered in the reference table Tyz.

[0064] 9, the control device 41 executes a result output step S4 after executing the third analysis step S3 described above. In the result output step S4, the control device 41 displays the remaining life Z determined for the inspection target component 10 on the display device 43.

[0065] 10, the damage factor data Dy may change for each second analysis step S2. Therefore, the remaining life Z may also change for each third analysis step S3. In the result output step S4, the control device 41 may display on the display device 43 the range ΔZ in which the remaining life Z is distributed.

[0066] The second embodiment also achieves the same effects as the first embodiment. Furthermore, in the second embodiment, the remaining life Z of the inspection target component 10 is estimated. Therefore, it is possible to clearly identify whether or when the inspection target component 10 needs to be repaired or replaced.

[0067] C: Third embodiment In the first embodiment, the magnetic property data Dx is generated from the measured noise characteristic F that represents the relationship between the excitation current E and the intensity V of Barkhausen noise. In the third embodiment, the control device 41 generates the magnetic property data Dx from the magnetic curve (B-H curve) of FIG. 12 that is measured for the inspection target member 10 in the first analysis step S1.

[0068] 12, the magnetic property data Dx of the third embodiment includes information such as the coercive force X4 or the saturation magnetic flux density X5 in the magnetic curve as the characteristic index Xm. The coercive force X4 is the strength of the magnetic field in the reverse direction required to further reduce the residual magnetization to zero when the magnetic field is reduced to zero after magnetization of the magnetic material. The saturation magnetic flux density X5 is the magnetic flux density when the magnetic material is magnetically saturated.

[0069] There is a correlation between the magnetic properties identified from the magnetic curve of the test object component 10 and the degree of creep damage in the test object component 10. Therefore, in the third embodiment, as in the first embodiment, there is a correlation between the magnetic property data Dx identified from the test object component 10 and the damage factor data Dy related to the test object component 10.

[0070] Fig. 13 shows an example of the correlation between magnetic property data Dx and damage factor data Dy. Fig. 13 illustrates the relationship between hardness Y5 and coercive force X4 of the test object 10. As can be seen from Fig. 13, the smaller the hardness Y5 (i.e., the more advanced the creep damage), the smaller the coercive force X4 tends to be.

[0071] In the second analysis step S2, the control device 41 generates damage factor data Dy from the magnetic property data Dx exemplified above. The specific procedure of the second analysis step S2 is the same as that in the first embodiment. The result output step S4 is also the same as that in the first embodiment.

[0072] The third embodiment also achieves the same effects as the first embodiment. The configuration of the second embodiment is similarly applied to the third embodiment. That is, in a third analysis step S3, the control device 41 identifies the remaining life Z of the inspection target component 10 from the damage factor data Dy, and displays the remaining life Z on the display device 43 in a result output step S4.

[0073] D: Modification Specific modified embodiments that can be added to each of the embodiments exemplified above are exemplified below. Two or more embodiments arbitrarily selected from the following examples may be combined as appropriate within the scope of not being mutually contradictory.

[0074] (1) In the above-described embodiments, the damage factor data Dy is identified from the magnetic property data Dx using a lookup table Txy that associates the magnetic property data Dx (Dx1, Dx2, ...) with the damage factor data Dy (Dy1, Dy2, ...). However, the method for identifying the damage factor data Dy from the magnetic property data Dx in the second analysis step S2 is not limited to the above example. For example, an arithmetic expression describing the relationship between the magnetic property data Dx and the damage factor data Dy may be used. In the second analysis step S2, the control device 41 calculates the damage factor data Dy by substituting the magnetic property data Dx into the arithmetic expression.

[0075] Furthermore, a learning model that has learned the relationship between the magnetic property data Dx and the damage factor data Dy may be used to generate the damage factor data Dy in the second analysis step S2. The learning model is, for example, a statistical model configured by a deep neural network, and has learned the relationship between the magnetic property data Dx and the damage factor data Dy through prior machine learning. In the second analysis step S2, the control device 41 processes the magnetic property data Dx using the learning model to generate the damage factor data Dy.

[0076] (2) In the second embodiment, the remaining lifespan Z was determined from the damage factor data Dy using a reference table Tyz that associates the damage factor data Dy (Dy1, Dy2, ...) with the remaining lifespan Z (Z1, Z2, ...). However, the method for determining the remaining lifespan Z from the damage factor data Dy in the third analysis step S3 is not limited to the above example. For example, an arithmetic expression that describes the relationship between the damage factor data Dy and the remaining lifespan Z may be used. In the third analysis step S3, the control device 41 calculates the remaining lifespan Z by substituting the damage factor data Dy into the arithmetic expression.

[0077] Furthermore, a learning model that has learned the relationship between the damage factor data Dy and the remaining lifespan Z may be used to identify the remaining lifespan Z in the third analysis step S3. The learning model is, for example, a statistical model configured by a deep neural network, and has learned the relationship between the damage factor data Dy and the remaining lifespan Z through prior machine learning. In the third analysis step S3, the control device 41 generates the remaining lifespan Z by processing the damage factor data Dy using the learning model.

[0078] (3) In the above-described embodiments, the magnetic property data Dx includes any one of a plurality of property indices Xm (X1 to X5) related to the magnetic properties of the inspected component 10. However, two or more of the property indices Xm (X1 to X5) related to the magnetic properties may be included in the magnetic property data Dx. Also, in the above-described embodiments, the damage factor data Dy includes any one of a plurality of damage indices Yn (Y1 to Y5) related to creep damage of the inspected component 10. However, two or more of the damage indices Yn (Y1 to Y5) related to creep damage may be included in the damage factor data Dy.

[0079] (4) Multiple remaining lives Z may be calculated from different characteristic indices Xm (X1 to X5) related to the inspection target component 10. For example, remaining life Zm1 is calculated from magnetic characteristic data Dx including characteristic index Xm1 (m1 = 1 to 5), and remaining life Zm2 is calculated from magnetic characteristic data Dx including characteristic index Xm2 (m2 = 1 to 5, m2 ≠ m1) that is different from characteristic index Xm1. Note that the type of damage index Yn included in damage factor data Dy is common to the calculation of remaining life Zm1 and remaining life Zm2.

[0080] Specifically, in a second analysis step S2, the control device 41 generates damage factor data Dy from the magnetic property data Dx including the characteristic index Xm1, and calculates the remaining life Zm1 from the damage factor data Dy in a third analysis step S3. Similarly, in the second analysis step S2, the control device 41 generates damage factor data Dy from the magnetic property data Dx including the characteristic index Xm2, and calculates the remaining life Zm2 from the damage factor data Dy in the third analysis step S3. Note that, although the above description illustrates the use of two different types of characteristic indexes Xm (Xm1, Xm2), the number of types of characteristic indexes Xm used to calculate the remaining life Z may be changed as desired.

[0081] In the result output step S4, the control device 41 displays the remaining life Zm1 and the remaining life Zm2 on the display device 43. The control device 41 may calculate a final remaining life Z by integrating the remaining life Zm1 and the remaining life Zm2, and display the resulting remaining life Z on the display device 43. For example, the control device 41 calculates the average value of the remaining life Zm1 and the remaining life Zm2 as the remaining life Z. The control device 41 may also display on the display device 43 the overlapping range of the range ΔZ of the multiple remaining lives Zm1 calculated from the characteristic index Xm1 and the range ΔZ of the multiple remaining lives Zm2 calculated from the characteristic index Xm2.

[0082] (5) Multiple remaining lives Z may be calculated from different damage indices Yn (Y1 to Y5) related to creep damage of the inspected component 10. For example, remaining life Zn1 is calculated from damage factor data Dy including damage index Yn1 (n1 = 1 to 5), and remaining life Zn2 is calculated from damage factor data Dy including a damage index Yn2 (n2 = 1 to 5, n2 ≠ n1) different from damage index Yn1. Note that the type of characteristic index Xm included in the magnetic characteristic data Dx is common to the calculation of remaining life Zn1 and remaining life Zn2.

[0083] Specifically, in the second analysis step S2, the control device 41 generates damage factor data Dy including a damage index Yn1 from the magnetic property data Dx, and calculates the remaining life Zn1 from the damage factor data Dy in the third analysis step S3. Similarly, in the second analysis step S2, the control device 41 generates damage factor data Dy including a damage index Yn2 from the magnetic property data Dx, and calculates the remaining life Zn2 from the damage factor data Dy in the third analysis step S3. Note that, although the above description illustrates the use of two different types of damage indexes Yn (Yn1, Yn2), the number of types of damage indexes Yn used to calculate the remaining life Z may be changed as desired.

[0084] In a result output step S4, the control device 41 displays the remaining life Zn1 and the remaining life Zn2 on the display device 43. The control device 41 may calculate a final remaining life Z by integrating the remaining life Zn1 and the remaining life Zn2, and display the resulting remaining life Z on the display device 43. For example, the control device 41 calculates the average value of the remaining life Zn1 and the remaining life Zn2 as the remaining life Z. The control device 41 may also display on the display device 43 the overlapping range between the range ΔZ of multiple remaining lives Zn1 calculated from the characteristic index Xm1 and the range ΔZ of multiple remaining lives Zn2 calculated from the characteristic index Xm2.

[0085] (6) In the above-described modification 4, the type of damage index Yn included in the damage factor data Dy is common to the calculation of the remaining life Zm1 and the calculation of the remaining life Zm2. Furthermore, in modification 5, the type of characteristic index Xm included in the magnetic property data Dx is common to the calculation of the remaining life Zn1 and the calculation of the remaining life Zn2. Modifications 4 and 5 may be combined. That is, the remaining life Z may be calculated for each different combination of the characteristic index Xm of the magnetic property data Dx and the damage index Yn of the damage factor data Dy.

[0086] Specifically, the control device 41 generates damage factor data Dy including a damage index Yn1 from magnetic property data Dx including characteristic index Xm1, and calculates the remaining life Z1 from the damage factor data Dy. Similarly, the control device 41 generates damage factor data Dy including a damage index Yn2 from magnetic property data Dx including characteristic index Xm2, and calculates the remaining life Z2 from the damage factor data Dy. The multiple remaining lives Z (Z1, Z2) may be displayed individually on the display device 43, or a final remaining life Z after merging may be displayed on the display device 43.

[0087] (7) The control device 41 may control the conditions for acquiring Barkhausen noise in the detection step S0 (hereinafter referred to as "observation conditions"). The observation conditions are, for example, conditions such as the frequency of the excitation current E or the frequency response of the filter processing for the detection signal Q.

[0088] For example, by appropriately controlling the observation conditions so as to reduce noise in the detection signal Q, in the first analysis step S1, the control device 41 can acquire magnetic property data Dx in which, among multiple properties (property indexes Xm) related to the measured noise characteristic F of Barkhausen noise, properties corresponding to specific factors related to creep damage in the test object component 10 are selectively emphasized. For example, magnetic property data Dx is generated in which a specific property index Xm, such as area X1, peak value X2, or peak current X3, is emphasized compared to other property indexes Xm. Therefore, the degree of creep damage in the test object component 10 (particularly damage related to a specific factor) can be identified with high accuracy.

[0089] 14, multiple peaks corresponding to different factors may be observed in the measurement noise characteristic F. The control device 41 approximates the measurement noise characteristic F using multiple distribution curves G (G1, G2). For example, the measurement noise characteristic F is approximated by a mixed Gaussian distribution that uses a Gaussian distribution as the distribution curve G.

[0090] Among the multiple peaks observed in the measurement noise characteristic F, those with large absolute values ​​of the current value of the excitation current E are likely to be peaks caused by precipitates inside the inspection target component 10 (i.e., one example of a factor of creep damage). Taking the above tendency into consideration, the control device 41 selects one distribution curve G (e.g., distribution curve G1 in FIG. 14 ) with a large absolute value of the current value of the excitation current E, where a peak exists, from among the multiple distribution curves G approximating the measurement noise characteristic F, and identifies the characteristic index Xm by analyzing the distribution curve G. The control device 41 generates magnetic characteristic data Dx including the identified characteristic index Xm from the distribution curve G.

[0091] The above process generates magnetic property data Dx in which properties corresponding to specific factors (e.g., precipitates) related to creep damage are selectively emphasized, compared with magnetic property data Dx generated directly from the initial measurement noise characteristics F. Therefore, the degree of creep damage (especially damage related to specific factors) in the test object 10 can be identified with high accuracy.

[0092] (8) As described above, the functions of the information processing device 40 according to each of the above embodiments are realized by cooperation between one or more processors constituting the control device 41 and a program stored in the storage device 42. The programs exemplified above can be provided in a form stored on a computer-readable recording medium and installed on a computer. The recording medium is, for example, a non-transitory recording medium, such as an optical recording medium (optical disk) such as a CD-ROM, but also includes any known form of recording medium, such as a semiconductor recording medium or a magnetic recording medium. Note that a non-transitory recording medium includes any recording medium other than a transitory, propagating signal, and does not exclude volatile recording media. Furthermore, in a configuration in which a distribution device distributes a program via a communication network, the recording medium storing the program in the distribution device corresponds to the non-transitory recording medium described above.

[0093] E: Notes From the above-described exemplary embodiments, the following configurations can be understood, for example.

[0094] A damage inspection method according to one aspect (Aspect 1) of the present disclosure includes a detection step of acquiring Barkhausen noise generated in a test object component by application of a magnetic field generated in an excitation coil by supplying an excitation current, a first analysis step of generating magnetic property data representing the characteristics of the Barkhausen noise, and a second analysis step of generating damage factor data relating to factors of creep damage generated in the test object component from the magnetic property data. In the above aspect, damage factor data relating to creep damage in the test object component is identified from the magnetic property data representing the characteristics of the Barkhausen noise generated in the test object component. Therefore, the degree of creep damage in the test object component can be identified easily and with high accuracy.

[0095] In a specific example (Aspect 2) of Aspect 1, the magnetic property data is data representing characteristics of the measurement noise characteristic that indicates the relationship between the current value of the excitation current and the intensity of the Barkhausen noise. In the above aspect, magnetic property data representing characteristics of the measurement noise characteristic that indicates the relationship between the current value of the excitation current and the intensity of the Barkhausen noise is generated. Since the measurement noise characteristic varies depending on the degree of creep damage of the inspected component, by using the magnetic property data representing the characteristics of the measurement noise characteristic, the degree of creep damage of the inspected component can be determined with high accuracy.

[0096] In a specific example (Aspect 3) of Aspect 2, the magnetic property data includes an area of ​​the measurement noise characteristic. The area of ​​the measurement noise characteristic varies depending on the degree of creep damage of the test object. Therefore, by using the magnetic property data including the area of ​​the measurement noise characteristic, the degree of creep damage of the test object can be determined with high accuracy.

[0097] In a specific example (Aspect 4) of Aspect 2 or Aspect 3, the magnetic property data includes a peak value in the measurement noise characteristic. The peak value in the measurement noise characteristic varies depending on the degree of creep damage of the test object component. Therefore, by using the magnetic property data including the peak value in the measurement noise characteristic, the degree of creep damage of the test object component can be determined with high accuracy.

[0098] In a specific example (Aspect 5) of any one of Aspects 2 to 5, the magnetic property data includes a current value of the excitation current corresponding to a peak in the measurement noise characteristic. The current value of the excitation current corresponding to a peak in the measurement noise characteristic (i.e., the position of the peak in the measurement noise characteristic) varies depending on the degree of creep damage of the inspected component. Therefore, by using magnetic property data including the current value of the excitation current corresponding to a peak in the measurement noise characteristic, the degree of creep damage of the inspected component can be determined with high accuracy.

[0099] In a specific example (Aspect 6) of any one of Aspects 1 to 5, a third analysis step of estimating the remaining life of the inspected component based on the damage factor data is further included. According to the above aspects, the remaining life of the inspected component is estimated. Therefore, it is possible to clearly identify whether or when the inspected component needs to be repaired or replaced.

[0100] In a specific example (Aspect 7) of any of Aspects 1 to 6, the damage factor data includes at least one of the amount of precipitation in the test component, the degree of creep void or dislocation occurrence in the test component, and the grain size or hardness of the test component. The creep damage factors exemplified above are significantly correlated with the characteristics of Barkhausen noise. Therefore, according to the above aspect, the degree of creep damage in the test component can be easily and accurately determined.

[0101] In a specific example (Aspect 8) of any of Aspects 1 to 7, by controlling the conditions for acquiring the Barkhausen noise in the detection step, the magnetic property data is acquired in the first analysis step in which a characteristic corresponding to a specific factor related to the creep damage is emphasized among the multiple characteristics of the Barkhausen noise. According to the above aspect, since a characteristic corresponding to a specific factor is emphasized among the multiple characteristics of the Barkhausen noise, the degree of creep damage related to the factor can be identified with high accuracy.

[0102] A damage inspection system according to one embodiment (embodiment 9) of the present disclosure comprises an excitation device that applies a magnetic field generated in an excitation coil by supplying an excitation current to an inspected component, and an inspection device that inspects the degree of creep damage in the inspected component, wherein the inspection device acquires Barkhausen noise generated in the inspected component, generates magnetic property data representing the characteristics of the Barkhausen noise, and generates damage factor data related to the factors of creep damage generated in the inspected component from the magnetic property data. [Explanation of symbols]

[0103] 100...damage inspection system, 10...component to be inspected, 11...surface, 20...excitation device, 21...excitation coil, 22...yoke, 221...protrusion, 222...protrusion, 223...base part, 23...detection coil, 24...detection coil, 30...inspection device, 31...current supply device, 32...signal processing device, 40...information processing device, 41...control device, 42...memory device, 43...display device, 44...operation device.

Claims

1. a detection step of acquiring Barkhausen noise generated in the test object by application of a magnetic field generated in an excitation coil by supplying an excitation current; a first analysis step of generating magnetic characteristic data representing the characteristics of the Barkhausen noise; a second analysis step of generating damage factor data relating to factors of creep damage occurring in the inspection target component from the magnetic property data; A damage inspection method including:

2. The magnetic characteristic data is data that represents characteristics of a measured noise characteristic that indicates a relationship between the current value of the excitation current and the intensity of the Barkhausen noise. The damage inspection method according to claim 1.

3. The magnetic property data includes an area of ​​the measurement noise property. The damage inspection method according to claim 2.

4. The magnetic property data includes a peak value in the measured noise property. The damage inspection method according to claim 2.

5. The magnetic characteristic data includes a current value of the excitation current corresponding to a peak of the measurement noise characteristic. The damage inspection method according to claim 2.

6. a third analysis step of estimating the remaining life of the inspected component based on the damage factor data; The damage inspection method of claim 1 further comprising:

7. The damage factor data includes at least one of the amount of precipitation in the test object component, the degree of occurrence of creep voids or dislocations in the test object component, and the grain size or hardness in the test object component. The damage inspection method according to claim 1.

8. By controlling the conditions for acquiring the Barkhausen noise in the detection step, the magnetic property data is acquired in the first analysis step in which a characteristic corresponding to a specific factor related to the creep damage is emphasized among a plurality of characteristics of the Barkhausen noise. The damage inspection method according to claim 1.

9. an excitation device that applies a magnetic field generated in an excitation coil by supplying an excitation current to the inspection target member; an inspection device for inspecting the degree of creep damage in the inspection target component, The inspection device includes: acquiring Barkhausen noise generated in the inspection target component; generating magnetic property data representing the characteristics of the Barkhausen noise; Damage factor data relating to factors of creep damage occurring in the inspected component is generated from the magnetic property data. Damage inspection system.

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