JP6989398B2 - Failure diagnostic device, failure diagnosis method, and failure diagnosis program - Google Patents

Description

Embodiments of the present invention relate to a failure diagnosis device, a monitoring device, a failure diagnosis method, and a failure diagnosis program.

In order to improve the operating rate of equipment in a power plant, the development of a failure diagnosis system for a power plant using IoT (Internet of Things) technology is being considered. As a result, a full-scale online failure diagnosis using abundant measurement data is realized, and it becomes possible to detect a failure or a failure sign of the device to be diagnosed at an early stage. However, in order to properly perform such a failure diagnosis, how to construct a database or an algorithm for accurately detecting a failure or a failure sign from the measurement data becomes a problem. Such a problem can also occur in various equipments that are greatly affected by the outage due to a failure, such as equipments in infrastructure equipment other than power plants.

Japanese Unexamined Patent Publication No. 2016-70272

Therefore, it is an object of the present embodiment of the present invention to provide a failure diagnosis device, a monitoring device, a failure diagnosis method, and a failure diagnosis program capable of accurately performing a failure diagnosis of a target device.

According to one embodiment, the failure diagnosis device has a simulated unit that outputs virtual measurement data when the target device is in at least one of one or more failure modes by simulating the target device. Be prepared. The device further includes a measurement data acquisition unit that acquires measurement data measured from the target device. The device further includes a failure mode identification unit that identifies a failure mode of the target device based on the virtual measurement data and the measurement data.

It is a schematic diagram which shows the structure of the failure diagnosis system of 1st Embodiment. It is a schematic diagram for demonstrating the operation of the generator model of 1st Embodiment. It is a flowchart which shows the operation of the failure diagnosis apparatus of 1st Embodiment. It is a graph for demonstrating the virtual measurement data of 1st Embodiment. It is another graph for demonstrating the virtual measurement data of 1st Embodiment. It is a graph for demonstrating the failure diagnosis method.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.

(First Embodiment)
FIG. 1 is a schematic diagram showing the configuration of the failure diagnosis system of the first embodiment. In the failure diagnosis system of FIG. 1, the failure diagnosis device 1 and the power plant 2 are connected by a communication network 3.

The failure diagnosis device 1 includes a control unit 1a such as a processor, a communication unit 1b such as a communication interface, an operation unit 1c such as a keyboard and a mouse, a display unit 1d such as a monitor, and a storage unit 1e such as a memory and storage. It has. The control unit 1a includes a simulation unit 11, a measurement data acquisition unit 12, a failure mode identification unit 13, and an output unit 14. These functional blocks of the control unit 1a are realized, for example, by executing a failure diagnosis program stored in the storage of the storage unit 1e by the processor of the control unit 1a. The failure diagnosis program may be installed in the failure diagnosis device 1 from the recording medium, or may be downloaded to the failure diagnosis device 1 from the server on the communication network 3.

The power plant 2 includes equipment such as a generator 2a, a turbine 2b, and a boiler 2c, and a control device 2d that controls the operation of these equipment. In the present embodiment, these devices are the target devices for diagnosis by the failure diagnosis device 1. An example of the power generation plant 2 is a thermal power generation plant, but the type of power generation is not limited to thermal power generation. The equipment targeted by the failure diagnosis device 1 is not limited to the power plant 2, and may be other equipment including general industrial equipment such as an engine, a compressor, and a pump.

The simulation unit 11 is a block that performs data processing that simulates the behavior of the target device, and performs such simulation using a model corresponding to the target device. The simulation unit 11 outputs virtual measurement data when the target device is in at least one of one or more failure modes by using a model. For example, when it is assumed that the rotor fan of the generator 2a is damaged, vibration data is output by simulation using the generator model.

The measurement data acquisition unit 12 is a block that acquires measurement data measured from the target device. For example, the vibration data measured from the generator 2a is transferred from the control device 2d to the measurement data acquisition unit 12 via the communication network 3.

The failure mode identification unit 13 receives virtual measurement data from the simulation unit 11, receives measurement data from the measurement data acquisition unit 12, and identifies the failure mode of the target device based on the received virtual measurement data and measurement data. .. For example, when the value of the virtual vibration data is close to the value of the vibration data, it is determined that the rotor fan of the generator 2a is damaged.

The data receiving unit that receives both the virtual measurement data and the measurement data may be included in the failure mode identification unit 13, or may be included in the failure diagnosis device 1 without being included. Further, the simulation unit 11, the measurement data acquisition unit 12, and the data reception unit are referred to as a monitoring device in the failure diagnosis device 1.

The output unit 14 is a block that outputs the failure mode identification result received from the failure mode identification unit 13. For example, the failure mode identification result can be displayed on the monitor of the display unit 1d or stored in the storage unit 1e. It can be saved or transmitted to the outside by the communication unit 1b. For example, the determination result that the rotor fan of the generator 2a is damaged is displayed on the monitor.

The output unit 14 may output a failure mode determined to have occurred in the target device as a result of identifying the failure mode, or output a failure mode determined to have occurred in the target device. May be. An example of the former is a monitor display of the determination result that "the rotor coil ventilation is obstructed and the rotor fan is damaged in the generator 2a". An example of the latter is a monitor display stating that "the generator 2a may have one or more of layer short circuit, rotor coil ventilation obstruction, and rotor fan damage".

FIG. 2 is a schematic diagram for explaining the operation of the generator model of the first embodiment.

In the generator model M of FIG. 2, one or more failure modes (assumed failure modes) D1 assumed for the generator 2a and operation data D2 obtained from the generator 2a are input, and a plurality of types of virtual measurements are performed. The value of the data D3 is output.

FIG. 2 shows failure modes A to M as an example of the assumed failure mode D1. For example, the failure mode A is a layer short of the generator 2a. The failure mode B is a rotor coil ventilation obstruction of the generator 2a. Failure mode C is damage to the rotor fan of the generator 2a. In the generator 2a, it is assumed that these failure modes occur. The number and types of failure modes are not limited to the number or number shown in FIG.

The operation data D2 is data representing the operation state of the generator 2a, and is transferred from the control device 2d to the simulation unit 11 via the communication network 3. An example of the operation data D2 is time-series data of the output power (electrical output) of the generator 2a.

FIG. 2 shows virtual measurement data A to G as an example of virtual measurement data D3. For example, the virtual measurement data A is the air gap magnetic flux density of the generator 2a. The virtual measurement data B is data (for example, amplitude and phase) related to the vibration of the rotor of the generator 2a.

The generator model M is configured as a set of various modules that output virtual measurement data D3 based on the assumed failure mode D1 and the operation data D2. FIG. 2 shows modules A to F as examples of these modules. For example, the module A is a module that handles the electromagnetic force of the generator 2a. Module B is a module that handles the thermal bending of the generator 2a. Module C is a module that handles the vibration response of the generator 2a. These modules are composed of, for example, mathematical formulas based on physical models, tables and functions based on the results of numerical calculations performed in advance, tables and functions based on experience, and the like.

When the failure diagnosis device 1 determines whether or not the failure mode A has occurred in the generator 2a (or whether or not the failure mode A has occurred, the same applies hereinafter), the data regarding the failure mode A is stored in the generator model M. Enter. In this case, the modules A and B output the virtual measurement data A and the data to the module C based on the data related to the failure mode A, and the module C outputs the virtual measurement data B based on the data from the modules A and B. Is output. The virtual measurement data A and B are used to determine whether or not the failure mode A has occurred in the generator 2a.

FIG. 3 is a flowchart showing the operation of the failure diagnosis device 1 of the first embodiment. FIG. 3 will be described by taking the case where the target device is the generator 2a as an example.

First, the generator model M of the simulated unit 11 outputs virtual measurement data D3 regarding a predetermined failure mode of the generator 2a based on the assumed failure mode D1 and the operation data D2 (process S1). For example, simulating unit 11, as virtual measurement data D3, and outputs the virtual measurement data X _i a failure mode i of the failure modes 1 to n. Here, n is an integer of 1 or more, and i is an integer satisfying 1 ≦ i ≦ n. Virtual measurement data X _i is a K order vector of values of K types of virtual measurement data (K is an integer of 2 or more). As can be understood from the loop processing of the processes Sa to Sb in FIG. 3, the failure diagnosis device 1 executes the processes S1 to S5 for each of the failure modes 1 to n. The failure mode i represents any failure mode among the failure modes 1 to n.

On the other hand, the measurement data acquisition unit 12 acquires and processes the measurement data measured from the generator 2a, and converts this data into processed measurement data X _{0 that can be compared with the virtual measurement data X i} (process S2). .. The processed measurement data X ₀ is a K-th order vector consisting of K types of processed measurement data values.

In process S2 for example, time-series data of vibration of the rotor of the generator 2a is Fourier transformed into the frequency data, the amplitude of a predetermined frequency in the frequency data is output as the processed measurement data X _0. Further, since the sampling frequency of the measurement data from the generator 2a may differ depending on the type of the measurement data, a process for matching the sampling frequencies is performed in the process S2. For example, in the above conversion process, the time-series waveform of the vibration measured at a high sampling frequency (for example, 512 Hz) is converted into a frequency waveform, and the overall amplitude of the rotor, the amplitude and phase of the vibration component synchronized with the rotation speed of the rotor, amplitude twice component of the rotational speed of the rotor, the amplitude of the vibration component of asynchronous rotation of the rotor, and these time rate of change is extracted from the frequency waveform is outputted as a component of the processed measurement data X _0. Furthermore, since the vibration of the rotor is related to the metal temperature of the bearing, the metal temperature may be output as the _{processed measurement data X 0.}

Next, simulation unit 11 normalizes the K values relationship between the virtual measurement data X _i, and outputs a normalized virtual measurement data x _i (process S3). Normalization, in order to accurately identify the failure mode i, is a process for changing the contribution ratio of the values between the virtual measurement data X _i (weight). For example, in the generator 2a, when the time change rate of the overall amplitude has a greater effect on the failure mode i than the overall amplitude, the value of the time change rate of the overall amplitude is multiplied by a coefficient according to the contribution rate. normalizing the virtual measurement data X _i to the virtual measurement data x _i. This makes it possible to accurately evaluate the influence of the overall amplitude and the time change rate on the failure mode i. Similarly, the measurement data obtaining unit 12 normalizes the relationship K values between the processed measurement data X _0, and outputs the processed measurement data x ₀ that is normalized (process S4). Hereinafter, the normalized processed measurement data x ₀ is simply referred to as “normalized measurement data x ₀ ”.

Normalized virtual measurement data x _i is a K order vector of values of K kinds of virtual measured data. Similarly, the normalized measurement data x ₀ is a K-th order vector consisting of values of K types of measurement data. Examples of the values (vector components) of the virtual measurement data x _i and the measurement data x ₀ are the overall amplitude of the rotor, the amplitude and phase of the vibration component synchronized with the rotation speed of the rotor, and the amplitude of the twice component of the rotation speed of the rotor. The amplitude of the vibration component asynchronous to the rotation of the rotor, the rate of change over time, the metal temperature of the bearing, etc. As the normalization method for the virtual measurement data X _{i and} the normalization method for the measurement data x ₀ , if the normalized data x _i and x ₀ that can be compared with each other can be derived, the same method is adopted. Alternatively, a different method may be adopted.

Next, the failure mode identification unit 13 receives the normalized virtual measurement data x _i and the normalized measurement data x _0, and calculates the difference squared sum e _i of the two (process S5). Sum of squared differences _{e i} is the difference (K order _vector) x i -x ₀ of the virtual measurement data _{x i} and the measurement data _{x 0} is calculated, the difference _x i -x ₀ norm _| x i -x ₀ | a It can be derived by calculating the square. In process S5, instead of the sum of the squares of the differences _x i -x _0, may be calculated inner product of _{x i} and _{x 0.}

The difference squared sum e _i becomes smaller as the measurement data x ₀ from the generator 2a is closer to the virtual measurement data x _{i in the failure mode i.} Therefore, the sum of squared differences e _i can be used to identify the failure mode i. Incidentally, the operation data D2 described above, instead used to calculate the virtual measurement data D3 in process S1, may be used to calculate the sum of squared difference e _i at process S5, the process S7 It may be used by specifying the operation data (for example, specifying the load factor) in.

The failure diagnosis device 1 performs processes S1 to S5 for each of the failure modes 1 to n. As a result, the sum of squared differences e1 to en _{corresponding to the failure modes 1 to n} is calculated. Failure mode identification section 13 rearranges these difference square sum _e 1 to e _n in ascending order (process S6). This corresponds to sorting the failure modes 1 to n in descending order of possibility of occurrence.

However, this process S6 is not an essential process, some if not sorting originally, even when the sorting, rather than the magnitude of the sum of squared differences e ₁ to e _n, for example, a predetermined priority In some cases, sorting is based on ranking. Therefore, the identification of the process S7 is also carried out in consideration of various situations including the presence or absence of the process S6.

Then, the failure mode identification section 13 identifies the failure mode of the generator 2a based on the sum of squared difference _e 1 to e _n (process S7). Specifically, failure mode identification unit 13 determines the difference square sum e _i is the smaller than the determination reference value obtained by normalizing, failure mode i occurs (or occurs might have) .. Since the sum of squared differences e ₁ to e _n are sorted in ascending order in the process S6, Order is m-th from the first (m is an integer satisfying 1 ≦ m ≦ n) criteria only difference square sum normalized the It may be compared with the value. That is, failure modes up to a predetermined order (mth) may be identified. In this case, a maximum of m failure modes will be identified.

The failure mode identification unit 13 indicates that the generator 2a has a layer short, and instead of determining the possibility that the generator 2a has a layer short, the failure mode identification unit 13 has a sign that the generator 2a has a layer short. It may be determined that it can be seen. This makes it possible to detect a sign of a layer short of the generator 2a before it occurs. In this way, the failure mode identification unit 13 may identify the failure mode in which the failure has occurred, or may identify the failure mode in which a sign of failure is seen. The latter identification process can be realized, for example, by setting the above-mentioned standardized determination reference value larger than that of the former identification process.

The term failure mode may be used as a term including only the case where a failure has occurred (for example, FMEA: Failure Mode and Effect Analysis), but in the present specification, when a failure has occurred. It is used as a term that can include the case where a sign of failure is seen. The failure mode identification unit 13 of the present embodiment may be configured to identify only the failure mode related to the failure that has occurred, or may identify the failure mode related to the failure that has occurred and have a sign of failure. It may be configured to identify the failure mode for.

After that, the output unit 14 outputs the failure mode identification result received from the failure mode identification unit 13. For example, the monitor may indicate that the generator 2a may have layer shorts, rotor coil ventilation obstruction, and rotor fan damage. In this case, these three failure modes may be displayed in the order obtained in the process S6, that is, in the order of the smallest sum of squared differences.

FIG. 4 is a graph for explaining the virtual measurement data of the first embodiment.

The horizontal axis of FIG. 4 shows a virtual measurement data x _i, the first component of the measured data x ₀ (x (1)), the vertical axis in FIG. 4, the second virtual measurement data x _i and the measurement data x ₀ The component (x (2)) is shown. FIG. 4 shows only the first and second components of the first to K components of the virtual measurement data x _i and the measurement data x _0. More precisely, the virtual measurement data x _i and the measurement data x ₀ are represented as points in the K-dimensional space.

The circles c _{1 to} c _{6 in} FIG. 4 are drawings of the judgment reference circles corresponding to the standardized judgment reference values used in the process S7. For example, when the measurement data x ₀ is located within the criteria circle c ₁ virtual measurement data x _1, the difference square sum e ₁ between the virtual measurement data x ₁ and the measurement data x ₀ is determined the reference circle Less than the radius of. Therefore, the failure mode 1 is identified in the process S7.

Since the judgment reference circle c ₂ and the judgment reference circle c _{5 in} FIG. 4 have overlapping portions, the measurement data x ₀ may be simultaneously located in both the judgment reference circle c ₂ and the judgment reference circle c _5. It is possible. In this case, both failure modes 2 and 5 are identified in process S7. The determination reference circle shown in FIG. 4 (specified by taking the case of K = 2 as an example) is represented as a K-dimensional sphere in the K-dimensional space.

In the present embodiment, as the determination reference circle c ₁ for virtual measurement data x _1, the first circle is a small circle, may be prepared second circle is larger circle. In this case, the first circle is used to determine that the generator 2a has a failure, and the second circle is used to determine that the generator 2a has a sign of failure. You may. Further, as the determination reference circle c ₁ for virtual measurement data x _1, are prepared two or more circles having different radii, determination may be performed two or more. This also applies to the determination reference circle _c 2 to c _6.

FIG. 5 is another graph for explaining the virtual measurement data of the first embodiment.

As shown in FIG. 5, paying attention to the difference squared sum e _{i of the virtual measurement data x i} and the measurement data x ₀ , the smaller the difference, the higher the possibility that a failure has occurred. It is also possible to estimate the failure probability according to the magnitude of the sum of squares e _i. FIG. 5 shows that the failure probability of the failure mode 1 corresponding to _{x 1} is larger than that of the failure modes 2 and 3 corresponding to _{x 2} and x _3. It is also possible to evaluate the size itself of the difference square sum e _i.

FIG. 6 is a graph for explaining a failure diagnosis method.

The signals 1 and 2 indicate signals obtained from the generator 2a, which is the target device for diagnosis, and correspond to the first and second components of the _{above-mentioned measurement data x 0, respectively.} The reference numerals A1 and A2 represent the threshold values of the signals 1 and 2, respectively. The curve A shows how the level of the signal 1 exceeds the threshold value A1 and the generator 2a becomes inoperable. In a general failure diagnosis method, when any of the signals 1 and 2 reaches the threshold values A1 and A2, it is determined that the generator 2a has failed or that the generator 2a shows a sign of failure. As a result, a situation such as curve A is avoided.

This situation is also illustrated in FIG. Region code R in FIG. 4, measuring a first magnitude of component (signal 1) the data x ₀ is less than the threshold value A1, the second component of the measured data x ₀ of the (signal 2) size is smaller than the threshold A2 Shows the area. When the measurement data x ₀ goes out of the region R, the occurrence of a failure or a sign thereof is detected.

On the other hand, in the failure diagnosis device 1 of the present embodiment, one or more failure modes that can occur in the generator 2a are assumed in advance, and the virtual measurement data obtained when these failure modes occur is used as a generator model. Derived using. Therefore, the failure diagnosis device 1 of the present embodiment can identify the failure mode of the generator 2a by comparing the virtual measurement data from the generator model with the measurement data from the generator 2a. Specifically, the failure diagnosis can be performed not by the region R as shown in FIG. 4 but by the determination reference circles c _{1 to} c _{6 as shown in FIG.}

The failure diagnosis device 1 of the present embodiment can identify the failure mode and detect the occurrence of a failure or a sign thereof. That is, even within the region R which is regarded as normal in the judgment using the threshold value, the specific failure location and its cause are specified before the event occurs by the integrated state quantity in which the signal 1 and the signal 2 are combined. can do.

As described above, in the present embodiment, instead of analyzing the measurement data to identify the failure mode, the virtual measurement data is derived from the assumed failure mode by the generator model, and the failure mode is compared between the virtual measurement data and the measurement data. Has been identified.

According to the failure diagnosis of the present embodiment, various advantages can be enjoyed.

For example, by analyzing past failure cases of the generator 2a and reflecting the analysis results in the assumed failure mode and the generator model, it is possible to accurately diagnose the failure of the generator 2a. The reason is that it is possible to analyze in detail what kind of measurement data can be obtained from the generator 2a at the time of failure or before the failure by analyzing the failure case, and the detailed analysis result is used for the assumed failure mode and the generator model. This is because it is reflected.

In addition, if the number of past failure cases and analysis results become more abundant, for example, by improving the above-mentioned normalization method and standardized judgment standard values, the accuracy of failure diagnosis can be further improved, or failures and failures can occur. It is also possible to detect a failure sign at an early stage. The normalization method and the standardized determination reference value may be changed by the user of the failure diagnosis device 1 by the operation unit 1c.

It should be noted that the failure of the equipment in the power plant 2 is likely to occur in the equipment having the rotor and the equipment producing the working fluid thereof. The reason is that the friction and vibration caused by the rotation of the rotor and the heat and pressure generated when the working fluid is generated affect the equipment. Therefore, it is desirable that the failure diagnosis device 1 uses these as the target devices for diagnosis.

As described above, according to the present embodiment, it is possible to accurately diagnose the failure of the target device.

Although some embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel devices, methods, and programs described herein can be implemented in a variety of other forms. In addition, various omissions, substitutions, and changes can be made to the forms of the apparatus, method, and program described in the present specification without departing from the gist of the invention. The appended claims and their equivalent scope are intended to include such forms and variations contained in the scope and gist of the invention.

1: Failure diagnosis device, 1a: Control unit, 1b: Communication unit,
1c: operation unit, 1d: display unit, 1e: storage unit,
2: Power plant, 2a: Generator, 2b: Turbine,
2c: Boiler, 2d: Control device, 3: Communication network,
11: Simulation unit, 12: Measurement data acquisition unit,
13: Failure mode identification unit, 14: Output unit

Claims (10)

A simulation unit that outputs virtual measurement data when the target device is in at least one of one or more failure modes by simulating the target device.
A measurement data acquisition unit that acquires measurement data measured from the target device,
A failure mode identification unit that identifies a failure mode of the target device based on the difference between the values of the plurality of types of the virtual measurement data and the values of the plurality of types of the measurement data.
Equipped with
In the failure mode identification unit, the target device fails based on whether or not the K-th order vector (K is an integer of 2 or more) corresponding to the difference is located in the K-dimensional sphere having the first radius. Whether or not the target device has a sign of failure based on whether or not the K-th order vector is located in a K-dimensional sphere having a second radius different from the first radius. Judging whether
Each component of the K-th order vector is a difference between the value of one type of the virtual measurement data and the value of one type of the measurement data.
Failure diagnosis device. The simulation unit normalizes the relationship between the values of the plurality of types of virtual measurement data.
The measurement data acquisition unit normalizes the relationship between the values of a plurality of types of the measurement data.
The failure diagnosis device according to claim 1, wherein the failure mode identification unit identifies the failure mode based on the normalized virtual measurement data and the normalized measurement data. The failure diagnosis device according to claim 1 or 2 , wherein the failure mode identification unit calculates the sum of squares or inner products of the differences and identifies the failure mode based on the sum of squares or the inner product. The claim mode identification unit arranges the failure modes in the order in which one or more failure modes are likely to occur based on the difference, and identifies the failure modes up to the predetermined order. The failure diagnosis device according to any one of 1 to 3. The simulation unit is a model in which one or more failure modes assumed for the target device and operation data obtained from the target device are input, and values of a plurality of types of virtual measurement data are output. The failure diagnosis device according to any one of claims 1 to 4 , wherein the simulation is performed using the device. The failure mode identification unit identifies the failure mode based on the virtual measurement data, the measurement data, and the output power of the generator, which is motion data obtained from the target device, according to claim 1. The failure diagnosis device according to any one of 4. The failure diagnosis device according to any one of claims 1 to 6 , further comprising an output unit that outputs the identification result of the failure mode. The failure diagnosis device according to claim 7 , wherein the failure mode identification result indicates a failure mode determined to have occurred in the target device or a failure mode determined to have occurred in the target device. The simulation unit outputs virtual measurement data when the target device is in at least one of one or more failure modes by simulating the target device.
The measurement data acquisition unit acquires the measurement data measured from the target device and obtains it.
Failure mode identification unit, based on a difference between the value of the measured data values and a plurality of types of a plurality of types of the virtual measured data, to identify the failure modes of the target device,
Look at including it,
In the failure mode identification unit, the target device fails based on whether or not the K-th order vector (K is an integer of 2 or more) corresponding to the difference is located in the K-dimensional sphere having the first radius. Whether or not the target device has a sign of failure based on whether or not the K-th order vector is located in a K-dimensional sphere having a second radius different from the first radius. Judging whether
Each component of the K-th order vector is a difference between the value of one type of the virtual measurement data and the value of one type of the measurement data.
Failure diagnosis method. The simulation unit outputs virtual measurement data when the target device is in at least one of one or more failure modes by simulating the target device.
The measurement data acquisition unit acquires the measurement data measured from the target device and obtains it.
Failure mode identification unit, based on a difference between the value of the measured data values and a plurality of types of a plurality of types of the virtual measured data, to identify the failure modes of the target device,
Look at including it,
In the failure mode identification unit, the target device fails based on whether or not the K-th order vector (K is an integer of 2 or more) corresponding to the difference is located in the K-dimensional sphere having the first radius. Whether or not the target device has a sign of failure based on whether or not the K-th order vector is located in a K-dimensional sphere having a second radius different from the first radius. Judging whether
Each component of the K-th order vector is a difference between the value of one type of the virtual measurement data and the value of one type of the measurement data.
A failure diagnosis program that causes a computer to execute a failure diagnosis method.

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