Abnormality diagnosis device and abnormality diagnosis method

Abstract

To provide a technique whereby anomaly diagnosis of a signal processing device, such as a control board, for performing a plurality of signal processes involving signal transfer and reception for operation of predetermined manufacturing equipment can be performed easily and reliably.SOLUTION: An anomaly diagnosis device 1 diagnoses an anomaly in a signal processing device that performs a plurality of signal processes involving signal transmission and reception for operation of predetermined manufacturing equipment. An anomaly diagnosis device 1 includes: a signal acquisition unit 11 configured to acquire a plurality of signals from the signal processing device; an anomaly score calculation unit 12 configured so that, using a learned model between signals generated by performing and leaning a sparse structure analysis on a plurality of signals when operation of the predetermined manufacturing equipment is normal, calculates an anomaly score of an actual result of each signal during operation of the predetermined manufacturing equipment acquired by the signal acquisition unit 11, the anomaly score indicating a deviation from a prediction by the learning model; and an anomaly determination unit 13 that determines whether the signal is anomalous nor not, based on the anomaly score calculated by the anomaly score calculation unit 12.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an abnormality diagnosis device for a signal processing device that performs a plurality of signal processes involving the transmission and reception of signals for the operation of a predetermined manufacturing facility, such as a control board. and Abnormality diagnosis method By law Regarding. [Background technology]

[0002] Manufacturing equipment used in manufacturing processes is equipped with field devices such as sensors, control circuit boards, or control devices that operate loads such as motors in response to instructions from programmed logic computers. Because control devices can fail due to external factors or aging, the devices themselves are equipped with functions to detect and display overcurrent, overvoltage, power loss, and sensor failure, which are useful for responding to abnormalities after they occur. The latest technology also uses functions that allow data from sensors, devices, and control circuit boards (control boards) to be collected using external data collection devices and used to check trend waveforms.

[0003] On the other hand, the control board is an electronic circuit board that operates a specific manufacturing facility using a control device and an information processing device, and when diagnosing an abnormality on the control board, the abnormality cannot be identified using only the above functions. Therefore, it takes time to troubleshoot the problem because the diagnosis of the abnormality is performed using the data stored in the control board itself that is not output externally, data collected from the sensors, and information such as overcurrent, overvoltage, power loss, and sensor failure indications.

[0004] Furthermore, for control devices for which the manufacturer has discontinued maintenance or withdrawn the equipment, there are no experts available, and even if the control board is examined at a later date, the root cause may not be identified. In such cases, this does not lead to improvements in response, such as the preparation of manuals after a failure. As a result, there is an issue of the time required to deal with problems through analysis (mean time to repair = MTTR) increasing.

[0005] To address this issue, for example, Patent Document 1 proposes a system that collects control board data and sensor data in advance, compares the data with expected values, and analyzes logs before and after an abnormality is detected.

[0006] Furthermore, as preventive maintenance, control boards are replaced periodically, elements that are expected to deteriorate are replaced or repaired, the capacity of the electrolytic capacitors removed at that time is investigated, deterioration trends are determined, and inspections of control device boards of the same age are prioritized. However, with the above preventive maintenance, even control boards that do not need repairs are repaired, which can lead to cost overflows, and in many cases testing is not possible due to a lack of a test environment after repairs, so the soundness of the control circuit cannot be completely guaranteed.

[0007] To address this issue, for example, in Patent Document 2, deterioration of electrolytic capacitors on a power supply board is diagnosed by measuring ripples in the power supply waveform and determining the deterioration of the programmer logic computer based on threshold values, leading to preventive maintenance. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-250594 [Patent Document 2] Japanese Patent Application Laid-Open No. 2003-248515 Summary of the Invention [Problem to be solved by the invention]

[0009] However, in Patent Document 1, the diagnosis method is based on an expected value, so depending on the settings, a failure may be overlooked. Also, only peripheral information is available at the time of the board failure, and it is not possible to detect which part of the board is the direct cause of the failure, so the problem of the time required for analysis to diagnose the true cause of the failure cannot be solved. On the other hand, the technology in Patent Document 2 is only for power supply devices, and no deterioration diagnosis technology for the control board itself has been established.

[0010] Therefore, the present invention provides a technology that can easily and reliably diagnose abnormalities in a signal processing device, such as a control board, that performs multiple signal processes involving the transmission and reception of signals for the operation of specified manufacturing equipment. [Means for solving the problem]

[0011] In order to solve the above problems, the present invention provides the following [1] to

[19] .

[0012] [1] An abnormality diagnosis device for diagnosing abnormalities in a signal processing device that performs multiple signal processes involving signal transmission and reception for the operation of a specified manufacturing facility, a signal acquisition unit that acquires a plurality of signals from the signal processing device; an anomaly score calculation unit that calculates an anomaly score representing a deviation of the actual results of each signal acquired by the signal acquisition unit during operation of the specified manufacturing equipment from a prediction made by the learning model, using a learned model between signals generated by performing sparse structure analysis on a plurality of signals when the specified manufacturing equipment is operating normally; an abnormality determination unit that determines whether a signal is abnormal based on the abnormality score calculated by the abnormality score calculation unit; An abnormality diagnosis device having the above.

[0013] [2] The trained model is an abnormality diagnosis device described in [1], which is trained by constructing a model structure in which each of a plurality of signals acquired by the specified manufacturing equipment under normal conditions can be an output, and other signals of the signal selected as an output from the plurality of signals are input, and performing the sparse structure analysis.

[0014] [3] The specified equipment is manufacturing equipment that transports transported objects, and the input signals used to create the trained model are stratified according to when the transported object is accelerating, in a steady state, or when it is decelerating. [1] An abnormality diagnosis device as described in [1] or [2].

[0015] [4] The abnormality diagnosis device according to any one of [1] to [3], wherein the signal processing device is a control board provided in a control device that controls the specified equipment.

[0016] [5] further comprising a map creation processing unit that creates a map that visualizes the anomaly scores of the plurality of signals; the map is a color map in which the horizontal axis represents time and the vertical axis represents the type of signal, and the degree of the abnormality score of the plurality of signals is represented by color gradation, The abnormality diagnosis device according to any one of [1] to [4], wherein the map display is capable of displaying the plurality of signals in units of acquisition cycles and enlarging the display up to a predetermined time.

[0017] [6] A method for diagnosing an abnormality in a signal processing device that processes a plurality of signals transmitted to and received from a signal processing device that performs a plurality of signal processes involving the transmission and reception of signals for the operation of a predetermined facility, comprising: a signal acquisition step of acquiring a plurality of signals from the signal processing device; an anomaly score calculation step of calculating an anomaly score representing a deviation of the actual results of each signal during operation of the specified manufacturing equipment acquired in the signal acquisition step from a prediction by the learning model, using a learned model between the signals generated by performing sparse structure analysis on the plurality of signals when the specified manufacturing equipment is operating normally; an abnormality determination step of determining whether or not the signal is abnormal based on the abnormality score calculated in the abnormality score calculation step; An abnormality diagnosis method comprising:

[0018] [7] The abnormality diagnosis method described in [6], wherein the trained model is constructed by constructing a model structure in which each of a plurality of signals acquired by the specified manufacturing equipment under normal conditions can be an output, and other signals of the signal selected as an output from the plurality of signals are input, and training is performed by performing the sparse structure analysis.

[0019] [8] further comprising a map creation processing step of creating a map that visualizes the anomaly scores of the plurality of signals; the map is a color map in which the horizontal axis represents time and the vertical axis represents the type of signal, and the degree of the abnormality score of the plurality of signals is represented by color gradation; The abnormality diagnosis method according to [6] or [7], wherein the map display can be expanded from a display in units of acquisition cycles of the plurality of signals to a predetermined time.

[0020] [9] A method for generating an abnormality diagnosis model for a signal processing device that performs a plurality of signal processes involving signal transmission and reception for operation of a predetermined facility, comprising: acquiring a plurality of signals from the signal processing device; A method for generating an abnormality diagnosis model, which constructs a model structure in which a signal other than a signal selected as an output from among the plurality of signals obtained when the specified manufacturing equipment is operating normally can be output, and generates a trained model between the signals by performing sparse structure analysis and training. [Effects of the Invention]

[0021] According to the present invention, a technology is provided that can easily and reliably diagnose abnormalities in a signal processing device, such as a control board, that processes multiple signals accompanied by signal transmission and reception for the operation of a specified manufacturing facility. This can prevent an increase in MTTR. Furthermore, the present invention can be applied to control devices that do not originally have a self-diagnosis function, and abnormality diagnosis is possible even without deep knowledge or skilled techniques of the device. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a functional block diagram of an abnormality diagnosis device according to an embodiment of the present invention; [Figure 2] FIG. 1 is a diagram for explaining sparse structure analysis. [Figure 3] FIG. 2 is a block diagram showing the main configuration of the static thyristor Leonard 2. [Figure 4]This figure shows an example in which the signal of the ACCR (Accelerated Current Control) in Figure 3 is used as the objective variable, all analyzable signals of the control device are structurally analyzed in a normalized form, and the top 16 explanatory variables with the highest influence on the objective variable are listed. [Figure 5] FIG. 10 is a diagram showing a two-dimensional map that is an example of a map of abnormality scores created by a map creation processing unit. [Figure 6] 2 is a flowchart showing a process flow when an abnormality diagnosis method is carried out by the abnormality diagnosis device of FIG. 1. [Figure 7] 10A and 10B are diagrams showing the results of an experiment in which characteristic changes due to deterioration of a capacitor in a control board were simulated to confirm whether they could be detected by an abnormality diagnosis device. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings.

[0024] <Abnormality diagnosis device> FIG. 1 is a functional block diagram of an abnormality diagnosis device according to one embodiment of the present invention. In this embodiment, an example will be described in which a control board of a static thyristor Leonard 2 is used as a signal processing device that performs a plurality of signal processes involving the transmission and reception of signals for the operation of a predetermined manufacturing facility.

[0025] The abnormality diagnosis device 1 takes in multiple signals from the control board (signal processing device) of the static thyristor Leonard 2, and creates a model between the signals by performing sparse structure analysis on the multiple signals under normal conditions and learning from them.The created model is then used to calculate an abnormality score for the signals under operation, normalizing and quantifying the deviation of the signals under operation from normality and determining whether the signals are abnormal.

[0026] The static Thyristor Leonard 2 is used, for example, in manufacturing equipment that transports objects such as steel plates by rotating a table motor with an electric motor. It is a control device that converts AC to DC and controls the voltage supplied to the DC motor (electric motor) to perform smooth speed control. The static Thyristor Leonard 2 has 10 or more control boards that can process (analyze) multiple signals, for example, several tens of signals, and is controlled by a higher-level control PLC 3. The control boards of the static Thyristor Leonard 2 have analog circuits that amplify and control signals from outside.

[0027] The upper control PLC 3 gives a control command to the control board of the static thyristor Leonard 2, and at the same time gives an analysis trigger to the abnormality diagnosis device 1, such as whether or not the device is in operation, and if the object is being transported, whether it is accelerating, in steady-state transport, or decelerating.

[0028] Next, the abnormality diagnostic device 1 will be specifically described. The abnormality diagnosis device 1 has an analysis unit 10 and a storage unit 20. The analysis unit 10 is configured as an analysis PLC, and has a signal sampling processing unit (signal acquisition unit) 11, an abnormality score calculation unit 12, an abnormality determination unit 13, an alarm output unit 14, and a map creation processing unit 15.

[0029] The signal sampling processing unit 11 receives an analysis trigger (collection start trigger) from the upper control PLC 3, and samples (acquires) signals from the control board, which is a signal processing device of the static thyristor Leonard 2, based on the analysis trigger.

[0030] The anomaly score calculation unit 12 uses a trained model between signals generated by performing sparse structure analysis on multiple signals when the specified manufacturing equipment is operating normally to calculate an anomaly score that represents the deviation of the actual results of each signal sampled by the signal sampling processing unit 11 during operation from the prediction by the trained model. Note that the normal state of the specified manufacturing equipment refers to a state in which the specified manufacturing equipment can operate normally using signals from a control board, which is a signal processing device. Furthermore, when applied to equipment that transports transport objects, the input signals used to create a model for normal operation are preferably stratified into (1) acceleration, (2) steady state (constant speed), and (3) deceleration.

[0031] Sparse structure analysis is a method for expressing causal relationships (correlations) between variables. Its unique feature is that it ignores weak correlations and retains only the essential relationships between strongly correlated variables. Output variables can be predicted by performing sparse structure analysis based on lasso regression, as shown in Figure 2. Figure 2(a) shows a schematic representation of a model under normal conditions. Lasso regression is performed on data from a certain section of the target signal, resulting in minute regression coefficients of 0. For example, the X2 variable is modeled so that it has no causal relationships (correlations) other than those between X1 and X4. A distinctive feature of this method is that it creates a model of all variables so that the regression coefficients between variables that do not have a causal relationship are 0. Figure 2(b) shows a schematic representation of an abnormal condition, in which the causal relationship (correlation) between X2 and X4 has been disrupted for some reason.

[0032] This will be explained in more detail. The trained model between signals is generated for a certain control board by an external offline computer (such as a server computer or cloud computer). The trained model between signals is generated by constructing a model structure in which each of multiple signals (e.g., X1 to X5 in Figure 2) acquired by a specific manufacturing facility under normal conditions can be an output, and the signal selected as an output from these multiple signals and other signals are used as inputs. Sparse structure analysis is then performed based on Lasso regression to train the model and predict the output variables. An anomaly score is then calculated, which indicates how much the actual performance of each signal acquired during operation deviates from the prediction based on the trained model. The anomaly score is defined for each signal using the following formula: Anomaly score = (Xi actual - Xi predicted) 2 / σ 2

[0033] In the case of an abnormality as shown in Figure 2(b), the value of this anomaly score increases, so this property is used to detect a breakdown in the causal relationship (correlation) between signals and thereby detect an anomaly.

[0034] This section provides a specific example of model generation for the control board of the static thyristor Leonard 2, which is used when the specified equipment is a conveying equipment for conveying steel plates, etc. Figure 3 is a block diagram showing the main components of the static thyristor Leonard 2. In this example, the ACCR signal shown in Figure 3 is used as the objective variable (input). A structural analysis was performed on all analyzable signals (50 signals) of the control equipment in a normalized form, and Figure 4 shows an example of the top 16 explanatory variables (outputs) with the highest influence on the objective variable. As shown in Figure 4, the explanatory variables are, from top to bottom, voltage feedback, ACR output, ACCR intermediate, forward rotation ON signal, etc. For each signal, normal signals are collected, and the top explanatory variables with the highest contribution are automatically selected to create a regression model.

[0035] In the case of conveying equipment that conveys steel plates and the like, as mentioned above, it is preferable that the input signals used to create a model under normal conditions be stratified and created for (1) acceleration, (2) steady state (constant speed), and (3) deceleration. In particular, in conveying equipment using static thyristor Leonard 2, the control board is a board with analog circuits, so the input signals used to create a model under normal conditions behave differently during acceleration, steady state (constant speed), and deceleration. For this reason, there is a strong need to stratify these and create multiple models.

[0036] The abnormality determination unit 13 is capable of setting a threshold for the abnormality score, and normalizes and quantifies the deviation of a signal from normality based on the abnormality score calculated for the signal during operation to determine whether the signal is abnormal. The abnormality determination unit 13 is capable of setting a threshold for the abnormality score. For example, the unit can determine that a signal is abnormal when the proportion of times the abnormality score is equal to or greater than the threshold during a data (signal) sampling period is equal to or greater than a predetermined value (e.g., 10%).

[0037] The alarm output unit 14 outputs an alarm contact output when the signal is determined to be abnormal based on the abnormality score, thereby issuing an external alarm.

[0038] The map creation processing unit 15 retrieves the data of multiple signals for which anomaly scores have been calculated from the storage unit 20 and creates a map of the anomaly scores of multiple signals over a certain period of time. The anomaly score map is sent to the web browser display PC 4 and displayed. The map is a visualization means for visualizing the anomaly scores of multiple signals over a certain period of time so that they can be understood at a glance. For example, as shown in FIG. 5, it is a two-dimensional map with time on the horizontal axis and signal type (11 types in FIG. 5) on the vertical axis, and is displayed so that each anomaly score value can be distinguished. While the map in FIG. 5 is displayed in grayscale, it is preferable to use a color map that displays color gradations according to the anomaly score value to make it easier to understand the anomaly scores. The map display can be expanded from a display in units of the signal sampling period to a predetermined time (e.g., up to one hour). This makes it possible to visualize overall anomalies, and also to use the zoom function to visualize instantaneous anomalies and identify which point in the signal in the control amplifier circuit first becomes abnormal.

[0039] <Abnormality diagnosis method> Next, a flow of the abnormality diagnosis method in the abnormality diagnosis device configured as above will be described.

[0040] FIG. 6 is a flowchart showing a process flow when the abnormality diagnosis method is carried out in the abnormality diagnosis device of FIG. First, an analysis trigger (collection start trigger), such as whether or not the object is being transported, the transport mode (accelerating, steady, decelerating), etc., is acquired from the upper control PLC 3 (step ST1), and a signal collection period is calculated (step ST2). The signal collection period is set for each of the acceleration, steady, and deceleration states.

[0041] Then, data (signals) from the control board, which is a signal processing device of the static thyristor Leonard 2, is stored (step ST3). This data is acquired and analyzed by the signal sampling processing unit 11 (step ST4). The sampling period of the signal at this time is preferably about 100 μs to 10 ms.

[0042] Next, the anomaly score calculation unit 12 calculates an anomaly score for each signal sampled by the signal sampling processing unit 11 during operation of the specified manufacturing equipment, using a trained model between signals generated by performing sparse structure analysis on the plurality of signals when the specified manufacturing equipment is normal (step ST5).The analysis results are stored in the storage unit 20 (step ST6).

[0043] The abnormality determination unit processing unit 13 calculates an abnormality score, for example, normalizes and quantifies the deviation from normality, and determines whether the signal is abnormal (step ST7). Specifically, if the proportion of abnormality scores equal to or greater than a threshold during the data collection period is equal to or greater than a predetermined value (for example, 10%), the signal is determined to be abnormal (score abnormal). If the score is determined to be abnormal, an alarm contact output is output from the alarm output unit 14 (step ST8), and an external alarm is issued (step ST9).

[0044] Next, the map of abnormality scores of multiple signals is updated in the map creation processing unit 15, an external web browser is started (step ST10), and the map display process of the external web browser display personal computer 4 is performed to update the display (step ST11).

[0045] The static thyristor Leonard 2 has more than 10 control boards as signal processing devices, and each control board is designed to collect multiple (dozens of) signals, resulting in the exchange of a huge amount of signals. For this reason, in the past, when an abnormality occurred, determining the abnormal signal, its cause, and the action to be taken required experience and skilled techniques, and was extremely complicated and difficult.

[0046] In contrast, in this embodiment, an abnormality diagnosis device 1 is installed, and a sparse structure analysis is performed on multiple signals during normal operation captured from the control board of the static thyristor Leonard 2. A model between the signals is generated using the resulting signal-to-signal model, and an abnormality score of the signal during operation is calculated, and an abnormality in the signal is determined based on the model. This allows for easy and reliable abnormality diagnosis of signal processing devices such as control boards without relying on experience or skilled techniques. This prevents the MTTR from increasing. Furthermore, this system is applicable to control devices that do not originally have a self-diagnosis function, and allows abnormality diagnosis even without deep knowledge of the device being diagnosed.

[0047] <Experimental Example> Next, we will explain an experimental example. Here, for the ACCR output processing of the ACR board of the static thyristor Leonard shown in Figure 3, we changed the ACCR gain assuming deterioration of the control board, simulating changes in characteristics due to deterioration of the capacitors in the control board, and confirmed whether the abnormality could be detected by the abnormality diagnosis device. Figure 7 shows the results of such an experimental example.

[0048] Figure 7(a) plots the difference between the predicted and actual ACCR output during operation at normal gain values ​​(prediction minus actual: difference between predicted and actual) and the ACCR gain anomaly score. The left side is a scatter plot of the difference between the predicted and actual ACCR gain output and the ACCR gain anomaly score, and the right side is a histogram of the difference between the predicted and actual ACCR gain output.

[0049] Figure 7(b) plots the difference between the predicted and actual values ​​and the ACCR gain anomaly score when the ACCR gain is changed to simulate capacitor degradation. Similarly, the left side is a scatter plot of the difference between the predicted and actual values ​​of the ACCR gain output and the ACCR gain anomaly score, and the right side is a histogram of the difference between the predicted and actual values ​​of the ACCR gain output.

[0050] As shown in Figure 7(a), the difference between the predicted and actual ACCR gain output and the ACCR gain anomaly score during operation at the normal gain value are small, but when the ACCR gain is changed to simulate capacitor degradation as shown in Figure 7(b), the anomaly score increases and the distribution of the difference between the predicted and actual ACCR output also doubles. This suggests that abnormality in the control board can be identified by monitoring the anomaly score.

[0051] <Other applications> Although the embodiments of the present invention have been described above, these are merely examples and should not be considered limiting. The above embodiments may be omitted, substituted, or modified in various ways without departing from the spirit of the present invention.

[0052] For example, in the above embodiment, a control board of a static thyristor Leonard is used as the signal processing device, but the control board of a control device other than a static thyristor Leonard may also be used, and the present invention is not limited to control boards, and can be applied to any signal processing device that performs multiple signal processes involving the transmission and reception of signals for the operation of specified manufacturing equipment.

[0053] Furthermore, although the conveying equipment for conveying objects such as steel plates has been given as an example of the predetermined equipment, the present invention is not limited to this. [Explanation of symbols]

[0054] 1. Abnormality diagnosis device 2. Static Thyristor Leonard 3 Upper control PLC 4 PC with web browser 10 Analysis Department 11 Signal sampling processing section 12 Anomaly score calculation section 13 Abnormality determination processing unit 14 Alarm output processing unit 15 Map creation processing section 20 Memory section

Claims

1. An abnormality diagnosis device for diagnosing abnormalities in a signal processing device that performs multiple signal processes involving signal transmission and reception for the operation of manufacturing equipment that transports transport objects using a DC motor, comprising: the signal processing device is a static thyristor Leonard control board that controls the supply of power to the DC motor to control the manufacturing equipment, a signal acquisition unit that acquires a plurality of signals from the control board that is the signal processing device; an anomaly score calculation unit that calculates an anomaly score representing a deviation of the actual results of each signal acquired by the signal acquisition unit during operation of the manufacturing equipment from a prediction made by the learned model, using a learned model between signals generated by performing sparse structure analysis on a plurality of signals when the manufacturing equipment is operating normally; an abnormality determination unit that determines whether a signal is abnormal based on the abnormality score calculated by the abnormality score calculation unit; and the trained model is a regression equation trained by constructing a model structure in which a signal selected from a plurality of signals acquired by the manufacturing equipment under normal conditions is used as a response variable and signals of the plurality of signals excluding the selected signal are used as explanatory variables, and performing the sparse structure analysis; An abnormality diagnosis device in which the input signal for creating the trained model is stratified according to when the transport object is accelerating, steady, or decelerating.

2. a map creation processing unit that creates a map that visualizes the anomaly scores of the plurality of signals; the map is a color map in which the horizontal axis represents time and the vertical axis represents the type of signal, and the degree of the abnormality score of the plurality of signals is represented by color gradation, 2. The abnormality diagnosis device according to claim 1, wherein the map display is capable of displaying the plurality of signals in units of acquisition cycles and enlarging the display up to a predetermined time.

3. A method for diagnosing abnormalities in a signal processing device that performs multiple signal processes involving signal transmission and reception for the operation of manufacturing equipment that transports an object to be transported by a DC motor, comprising: the signal processing device is a static thyristor Leonard control board that controls the supply of power to the DC motor to control the manufacturing equipment, a signal acquisition step of acquiring a plurality of signals from the control board which is the signal processing device; an anomaly score calculation step of calculating an anomaly score representing a deviation of the actual results of each signal during operation of the manufacturing equipment acquired in the signal acquisition step from a prediction by the learned model, using a learned model between the signals generated by performing sparse structure analysis on the plurality of signals when the manufacturing equipment is operating normally; an abnormality determination step of determining whether or not the signal is abnormal based on the abnormality score calculated in the abnormality score calculation step; and the trained model is a regression equation trained by constructing a model structure in which a signal selected from a plurality of signals acquired by the manufacturing equipment under normal conditions is used as a response variable and signals of the plurality of signals excluding the selected signal are used as explanatory variables, and performing the sparse structure analysis; An abnormality diagnosis method, in which the input signal for creating the trained model is stratified according to when the transport object is accelerating, steady, or decelerating.

4. a map creation processing step of creating a map that visualizes the anomaly scores of the plurality of signals; the map is a color map in which the horizontal axis represents time and the vertical axis represents the type of signal, and the degree of the abnormality score of the plurality of signals is represented by color gradation; 4. The abnormality diagnosis method according to claim 3, wherein the map display can be expanded from a display in units of acquisition cycles of the plurality of signals to a predetermined time.

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