Determination system, determination method, and program

Abstract

An objective of the present is to provide a determination system that determines an abnormal state on the basis of waveform data, in a general-purpose manner. A determination system (1) comprises an acquiring unit (11), a feature calculating unit (12), a learning unit (14), and a determining unit (15). The acquiring unit (11) acquires waveform data (31) from a sensor (22) installed in a production facility (2). The feature calculating unit (12) calculates a plurality of features (33) from the waveform data (31). The learning unit (14) generates a trained model on the basis of the plurality of features (33) of each of a plurality of sets of reference waveform data (41). The plurality of sets of reference waveform data (41) are acquired from the sensor (22). The determining unit (15) determines whether the operation of the production facility (2) corresponding to the waveform data (31) is normal or abnormal on the basis of a degree of abnormality calculated from the plurality of features (33) of the waveform data (31) using the trained model.

Description

Determination System, Determination Method, and Program

[0001] The present disclosure relates to a determination system, a determination method, and a program, and particularly to a determination system, a determination method, and a program that use waveform data.

[0002] Patent Document 1 discloses a burr detection device that detects burrs (abnormalities) in a press device (production equipment). In the burr detection device of Patent Document 1, the presence or absence of burrs is determined based on the displacement of a stripper that holds and presses a workpiece.

[0003] However, in the burr detection device of Patent Document 1, when the stripper crushes the burr or the displacement of the stripper is small, the burr may not be detected even if it occurs. On the other hand, in the burr detection device of Patent Document 1, if the threshold value of the displacement of the stripper is small, there is a possibility of false detection of burrs when the operation of the press device is normal.

[0004] Also, with general criteria such as the maximum value and integral value of waveform data, it is necessary to set the criteria according to each die and workpiece, and it is difficult to use uniform criteria for multiple production facilities.

[0005] Japanese Patent Application Laid-Open No. 2015-412

[0006] An object of the present disclosure is to provide a determination system, a determination method, and a program that generally determine an abnormal state based on waveform data.

[0007] A determination system according to an aspect of the present disclosure includes an acquisition unit, a feature quantity calculation unit, a learning unit, and a determination unit. The acquisition unit acquires waveform data from a sensor installed in a production facility. The feature quantity calculation unit calculates a plurality of feature quantities from the waveform data. The learning unit generates a learned model based on the plurality of feature quantities of each of the plurality of reference waveform data. The plurality of reference waveform data are acquired from the sensor. The determination unit determines whether the operation of the production facility corresponding to the waveform data is normal or abnormal based on the degree of abnormality calculated from the plurality of feature quantities of the waveform data using the learned model.

[0008] A determination method according to one aspect of this disclosure includes an acquisition step, a feature calculation step, and a determination step. In the acquisition step, waveform data is acquired from a sensor installed on the production equipment. In the feature calculation step, a plurality of features are calculated from the waveform data. In the determination step, a trained model generated based on a plurality of reference waveform data is used to determine whether the operation of the production equipment corresponding to the waveform data is normal or abnormal, based on the degree of abnormality calculated from the plurality of features of the waveform data. The plurality of reference waveform data are acquired from the sensor.

[0009] A program according to one aspect of this disclosure causes one or more processors to execute the determination method.

[0010] Figure 1 is a functional block diagram of the determination system according to Embodiment 1. Figure 2 is a schematic diagram of the production equipment whose operation is determined by the determination system. Figure 3 is an example of waveform data in the determination system. Figure 4 is a schematic diagram showing an example of Mahalanobis distance. Figure 5 is a flowchart of the operation of the determination system. Figure 6 is a schematic diagram of the production equipment whose operation is determined by the determination system of Embodiment 2. Figure 7 is an example of partial data in the determination system. Figure 8 is a flowchart of part of the operation of the determination system of Embodiment 3. Figure 9 is a flowchart of another part of the operation of the determination system.

[0011] The determination system, determination method, and program according to the embodiments will be described in detail below with reference to the drawings. However, the figures described in the embodiments below are schematic diagrams, and the ratios of the size and thickness of each component do not necessarily reflect the actual dimensional ratios. The configurations described in the embodiments below are merely examples of the disclosure. The disclosure is not limited to the embodiments below, and various modifications are possible depending on the design, etc., as long as the effects of the disclosure can be achieved.

[0012] (Embodiment 1) (1) Overview The judgment system 1 (see Figure 1) is a system that determines whether the operation of the production equipment 2 (see Figure 2) is in an abnormal state based on waveform data 31 (see Figure 3) that shows one of a plurality of operations that are repeatedly performed in the production equipment 2 (see Figure 2). The production equipment 2 is, for example, a die press machine that uses a die 21. Specifically, an abnormal state in the operation of the production equipment 2 refers to a slag ejection failure in the die 21. Slag ejection refers to the fact that in a hole-punching process using the die 21, a portion 24 of the punched material 23 (hereinafter referred to as "slag") is not discharged properly and remains inside the die 21. A slag ejection failure means that the processing of the material 23 cannot be performed properly because of the presence of slag 24 inside the die 21.

[0013] A sensor 22 is installed in the production equipment 2. The sensor 22 outputs sensor data 32 that indicates multiple operations that are repeatedly performed in the production equipment 2. Specifically, the sensor 22 is a strain sensor that detects the strain of the mold 21. The sensor data 32 is data that shows the time series of the strain value of the mold 21. The sensor data 32 includes one or more waveform data 31. Specifically, one waveform data 31 is the part of the sensor data 32 that indicates one operation among the operations that are repeatedly performed in the production equipment 2. Among the multiple pressing operations, the part from the start time t0 (see Figure 3) to the end time of one pressing operation corresponds to one waveform data 31. The waveform data 31 is discontinuous data that has a start point and an end point. Here, "discontinuous data" means data of finite length. Also, the waveform data 31 is data that shows the time series change of one or more values. In Embodiment 1, the waveform data 31 shows the time series change of the strain value of the mold 21. Note that the strain values ​​in Figure 3 are relative to the reference value.

[0014] (2) Configuration of the Judgment System The judgment system 1, as shown in Figure 1, comprises an acquisition unit 11, a feature calculation unit 12, a storage unit 13, a learning unit 14, a judgment unit 15, and an output unit 16.

[0015] The acquisition unit 11 acquires waveform data 31. Specifically, the acquisition unit 11 acquires sensor data 32 from the sensor 22 and extracts one or more waveform data 31 from the sensor data 32.

[0016] As shown in Figure 3, the waveform data 31 is data that shows the time-series change of a single data point. Here, the single data point is the strain value of the mold 21.

[0017] The acquisition unit 11 acquires information from the production equipment 2, for example, indicating the start and end times of each of a plurality of repeated operations, and extracts one or more waveform data 31. For example, if the production equipment 2 is a die press machine, the acquisition unit 11 acquires the height of the stripper or the rotation angle of the crank that operates the stripper, and extracts one or more waveform data 31 from the sensor data 32. The sensor 22 may also output one waveform data 31 each time the production equipment 2 performs each of the plurality of operations, in conjunction with the operation of the production equipment 2.

[0018] The feature calculation unit 12 calculates multiple features 33 from the waveform data 31. Specifically, the feature calculation unit 12 calculates two or more features 33 from among the multiple types of features 33 described below. For example, the feature calculation unit 12 extracts partial data from the waveform data 31 and calculates the features 33.

[0019] Specifically, the feature calculation unit 12, for example, acquires the operating timing from the production equipment 2 and extracts partial data from the waveform data 31. Alternatively, the feature calculation unit 12 may calculate the feature 33 using a trained model that outputs a feature 33 when waveform data 31 or partial data is input. Partial data is the portion of the waveform data 31 whose waveform changes depending on whether the operation of the production equipment 2 is normal or abnormal. Here, "partial data is the portion of the waveform data 31 whose waveform changes depending on whether the operation of the production equipment 2 is normal or abnormal" means that the partial data includes a portion of the waveform data 31 whose waveform changes depending on whether the operation of the production equipment 2 is normal or abnormal, or a portion whose waveform tends to change easily. Partial data may include, for example, the initial waveform portion 351, the waveform corresponding to the stripper process 352, the waveform corresponding to the cutting process 353, or the waveform corresponding to the bottom dead center process 354, as will be described later.

[0020] The feature calculation unit 12 calculates the features 33 of the partial data from the partial data, including values ​​contained in the partial data or values ​​calculated from the values ​​contained in the partial data. Values ​​calculated from the values ​​contained in the partial data include, for example, the rate of change, integral value, frequency features, entropy, variance, and curve fitting coefficient. Frequency features include, for example, spectral energy and values ​​that can be calculated by the Fast Fourier Transform (FFT).

[0021] One of the multiple feature quantities 33 is, for example, the integral value of the initial waveform portion 351 of the waveform data 31, as shown in Figure 3. The initial waveform portion 351 is the part of the waveform data 31 that corresponds to the period from time t1 to time t2, before the stripper comes into contact with the material 23 in the production equipment 2. Another of the multiple feature quantities 33 is a value calculated from the values ​​contained in the initial waveform portion 351, which is a partial data, for example, the time rate of change of the initial waveform portion 351 of the waveform data 31. The time rate of change of the initial waveform portion 351 is the slope of the initial waveform portion 351 in a graph where the horizontal axis is time and the vertical axis is the value of the waveform data 31. If no scraping occurs, the material 23 does not come into contact with the stripper during the period from time t1 to time t2. In contrast, if scraping occurs, the stripper may come into contact with the material 23 during the period from time t1 to time t2. Therefore, when scraping occurs, the value of the feature quantity 33 tends to change more than when scraping does not occur.

[0022] Furthermore, one of the multiple feature quantities 33 is, for example, a value calculated from the value included in the waveform 352 corresponding to the stripper process among the partial data, waveform data 31, as shown in Figure 3, and is the integral value of waveform 352. The waveform 352 corresponding to the stripper process is the portion of the waveform data 31 that corresponds to the period from time t2 to time t3 when the stripper presses the material 23 in the production equipment 2. When sludge buildup occurs, sludge 24 may get stuck between the stripper and the mold 21, so the value of feature quantity 33 tends to change more than when sludge buildup does not occur.

[0023] Furthermore, one of the multiple feature quantities 33 is, for example, as shown in Figure 3, a value included in the waveform 353 corresponding to the punching process within the waveform data 31, which is partial data, and is the maximum value of waveform 353. The waveform 353 corresponding to the punching process is the portion of the waveform data 31 that corresponds to the period from time t4 to time t5 during which the stripper punches out the material 23 in the production equipment 2. When slag buildup occurs, slag 24 may get stuck between the stripper and the die 21, so the value of feature quantity 33 tends to change more than when slag buildup does not occur.

[0024] Furthermore, one of the multiple feature quantities 33 is, for example, a value included in the waveform 354 corresponding to the bottom dead center process of the waveform data 31, which is partial data, as shown in Figure 3, and is the maximum value of the waveform 354. The waveform 354 corresponding to the bottom dead center process is the portion of the waveform data 31 that corresponds to the period from time t6 to time t7 when the stripper has fully lowered in the production equipment 2. When slag buildup occurs, deformation of the mold 21 may occur due to the slag 24, so the value of feature quantity 33 tends to change more than when slag buildup does not occur.

[0025] The memory unit 13 is a storage medium that stores multiple waveform data. The multiple waveform data includes waveform data 31 for determining whether the operation of the corresponding production equipment 2 is normal or abnormal. The multiple waveform data also includes multiple reference waveform data 41. The multiple reference waveform data 41 are waveform data in which the operation of the corresponding production equipment 2 is normal. Here, "waveform data in which the operation of the corresponding production equipment 2 is normal" refers to waveform data that corresponds to the operation of the production equipment 2 in such a way that the processed material 23 does not become a defective product, and does not necessarily mean that there are absolutely no abnormalities in the operation of the production equipment 2 corresponding to the reference waveform data. The memory unit 13 also stores feature quantities 33 corresponding to each of the multiple reference waveform data 41.

[0026] The learning unit 14 generates a trained model based on the multiple feature quantities 33 of each of the multiple reference waveform data 41. The trained model includes, for example, a unit space 51. Here, the unit space 51 is a region in a multidimensional space with each of the multiple feature quantities 33 as an axis, where points corresponding to the reference waveform data 41 are gathered. For example, if the multiple feature quantities 33 include two feature quantities 33, a first feature quantity and a second feature quantity, the unit space 51 is a region in a two-dimensional plane, as shown in Figure 4.

[0027] Here, the multiple feature quantities 33 calculated by the feature quantity calculation unit 12 from the waveform data 31 and the multiple feature quantities 33 of each of the multiple reference waveform data 41 used to generate the unit space 51 correspond to each other. More specifically, one feature quantity 33 of the waveform data 31 and one feature quantity 33 of one reference waveform data 41 that correspond to each other represent the same feature of the waveform data 31 or the one reference waveform data 41. For example, the multiple feature quantities 33 calculated by the feature quantity calculation unit 12 from the waveform data 31 and each of the multiple feature quantities 33 of the multiple reference waveform data 41 include the integral value of the initial waveform section 351 and the maximum value of the waveform 354 corresponding to the bottom dead center. The integral value of the initial waveform section 351 of the waveform data 31 and the integral value of the initial waveform section 351 of one reference waveform data 41 correspond to each other. The maximum value of the waveform 354 corresponding to the bottom dead center of the waveform data 31 and the maximum value of the waveform 354 corresponding to the bottom dead center of one reference waveform data 41 correspond to each other.

[0028] The determination unit 15 determines whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal, based on the abnormality score calculated from multiple feature quantities 33 of the waveform data 31. More specifically, the determination unit 15 calculates the abnormality score from multiple feature quantities 33 of the waveform data 31 using a trained model generated by the learning unit 14. The abnormality score based on the multiple feature quantities 33 is, for example, based on the multiple feature quantities 33 and the unit space 51. Specifically, the abnormality score based on the multiple feature quantities 33 is the Mahalanobis distance d1 (see Figure 4) or Euclidean distance between the multiple feature quantities 33 and the unit space 51, or the cosine similarity between the multiple feature quantities 33 and the unit space 51. The trained model including the unit space 51 may also be a support vector machine (SVN), and the abnormality score based on the multiple feature quantities 33 is a value indicating whether or not it is an outlier. The trained model may also be created using k-nearest neighbors, neural networks (NN), or clustering. In this embodiment, the degree of abnormality is the Mahalanobis distance d1 between the multiple feature quantities 33 and the unit space 51. As shown in Figure 4, the Mahalanobis distance d1 is the distance between a point corresponding to one of the multiple feature quantities 33 of the waveform data 31 and the unit space 51 in a multidimensional space with each of the multiple feature quantities 33 as an axis. The determination unit 15 determines that the operation of the production equipment 2 corresponding to the waveform data 31 is abnormal if the Mahalanobis distance d1 is greater than or equal to a threshold. The determination unit 15 also determines that the operation of the production equipment 2 corresponding to the waveform data 31 is normal if the Mahalanobis distance d1 is less than a threshold.

[0029] More specifically, the learning unit 14 and the determination unit 15 perform the following processes, for example.

[0030] The learning unit 14 normalizes each feature 33 of one or more reference waveform data 41. Here, the feature calculation unit 12 calculates k (k is an integer of 2 or more) feature 33 for each of the waveform data 31 and one or more reference waveform data 41. The k feature 33 include, for example, the integral value of the initial waveform unit 351 and the maximum value of the waveform 354 corresponding to the bottom dead center. The number of reference waveform data 41 is n (n is an integer of 2 or more). That is, k feature 33 are calculated for each of the n reference waveform data 41.

[0031] The learning unit 14 normalizes the n reference waveform data 41. Specifically, for each of the k feature quantities 33, it normalizes them so that the mean of the n feature quantities 33 is 0, and the standard deviation of the n feature quantities 33 is 1.

[0032] Specifically, the normalized feature Yij of the j-th feature (j is an integer between 1 and k) among the k feature quantities 33, which corresponds to the i-th reference waveform data 41 (i is an integer between 1 and n) among the n reference waveform data 41, is expressed by the following formula, using the j-th feature quantity yij corresponding to the i-th reference waveform data 41, the mean value yj of the j-th feature quantity 33, and the standard deviation σj of the j-th feature quantity 33.

[0033] Yij = (yij - yj) / σj ... (Equation 1) Here, the mean value yj of the j-th feature 33 is the arithmetic mean of the j-th feature y1j to ynj corresponding to the n reference waveform data 41. Also, the standard deviation σj of the j-th feature 33 is the standard deviation of the j-th feature y1j to ynj corresponding to the n reference waveform data 41. Note that the j-th feature yij corresponding to the i-th reference waveform data 41 represents the same feature of the waveform data for all i, for example, the integral value of the initial waveform section 351.

[0034] The learning unit 14 calculates the normalized feature Yij for all i and j using the above (Equation 1).

[0035] Next, the learning unit 14 creates a correlation coefficient matrix. The correlation coefficient matrix is ​​a k x k square matrix. The diagonal elements of the correlation coefficient matrix are 1. Also, the elements r in the p x q column of the correlation coefficient matrix (where p and q are integers less than or equal to k, and p ≠ q) ｐｑ It is given by the following formula.

[0036]

[0037] Next, the learning unit 14 calculates the inverse matrix of the correlation coefficient matrix.

[0038] The determination unit 15 normalizes each feature 33 of the waveform data 31. Here, the determination unit 15 performs normalization using the same criteria as the feature 33 of the n reference waveform data 41. That is, the j-th normalized feature Ytj of the waveform data 31 is expressed by the following formula, using the j-th feature ytj of the waveform data 31, the mean value yj of the j-th feature 33, and the standard deviation σj of the j-th feature 33.

[0039] Ytj = (ytj - yj) / σj ... (Equation 3) Next, the determination unit 15 calculates the Mahalanobis distance d1 (see Figure 4) using the inverse of the correlation coefficient matrix and the feature quantities 33 of the normalized waveform data 31. The Mahalanobis distance d1 is given by the following equation using the normalized feature quantity Ytj of the waveform data 31 and the inverse of the correlation coefficient matrix A.

[0040]

[0041] The determination unit 15 determines whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal by determining whether the calculated Mahalanobis distance d1, which is the degree of abnormality, is above or below a threshold. Specifically, if the Mahalanobis distance d1 is above a threshold, the determination unit 15 determines that the operation of the production equipment 2 corresponding to the waveform data 31 is abnormal. On the other hand, if the Mahalanobis distance d1 is below a threshold, the determination unit 15 determines that the operation of the production equipment 2 corresponding to the waveform data 31 is normal.

[0042] The output unit 16 outputs the determination result of the determination unit 15 to the storage unit 13. The output unit 16 also outputs the multiple feature quantities 33 used for the determination to the storage unit 13. In other words, the storage unit 13 further stores the determination result of the determination unit 15 and the multiple feature quantities 33.

[0043] When it is determined that the operation of the production facility 2 corresponding to the waveform data 31 is abnormal, for example, the output unit 16 outputs a stop command to the production facility 2. Further, the output unit 16 performs an output for presenting the determination result to, for example, the operator of the production facility 2. The output unit 16 has, for example, a display control function for causing a display device to display an image showing the determination result to the operator of the production facility 2. The image includes, for example, the presence or absence of occurrence of an abnormality in the operation of the production facility 2, the occurrence frequency or occurrence tendency of the abnormality, and candidates for abnormal locations of the production facility 2 assumed from the occurrence frequency or occurrence tendency of the abnormality. The display device may be a display included in the determination system 1 or an information terminal device capable of communicating with the determination system 1.

[0044] The determination system 1 includes a computer system. The computer system mainly consists of a processor and memory as hardware. The function of the determination system 1 in this disclosure is realized by the processor executing a program recorded in the memory of the computer system. The program may be pre-recorded in the memory of the computer system, provided via a telecommunications line, or provided on a non-temporary recording medium such as a memory card, optical disk, or hard disk drive that can be read by the computer system. The processor of the computer system consists of one or more electronic circuits including semiconductor integrated circuits (ICs) or large-scale integrated circuits (LSIs). The integrated circuits such as ICs and LSIs referred to here are named differently depending on the degree of integration, and include integrated circuits called system LSIs, VLSIs (Very Large Scale Integrations), or ULSIs (Ultra Large Scale Integrations). Furthermore, FPGAs (Field-Programmable Gate Arrays) that are programmed after the manufacture of the LSI, or logic devices that allow for the reconstruction of junction relationships or circuit compartments within the LSI, can also be used as processors. Multiple electronic circuits may be integrated onto a single chip or distributed across multiple chips. Multiple chips may be integrated onto a single device or distributed across multiple devices. The computer system referred to here includes a microcontroller having one or more processors and one or more memories. Therefore, the microcontroller also consists of one or more electronic circuits, including semiconductor integrated circuits or large-scale integrated circuits.

[0045] In addition, it is not an essential configuration of the determination system 1 that a plurality of functions in the determination system 1 are aggregated in one housing. The components of the determination system 1 may be provided dispersedly in a plurality of housings. Furthermore, at least some functions of the determination system 1, for example, some functions of the determination unit 15 may be realized by a cloud (cloud computing) or the like. Also, part or all of the storage unit 13 may be realized by cloud storage, a database server, or the like.

[0046] On the contrary, at least some functions of the determination system 1 that are dispersed in a plurality of components in Embodiment 1 may be aggregated in one housing.

[0047] (3) Operation diagram 5 is a flowchart showing the operation of the determination system 1 according to Embodiment 1.

[0048] The acquisition unit 11 of the determination system 1 acquires waveform data 31 (step ST1). The acquisition unit 11, for example, acquires sensor data 32 from the sensor 22, acquires the start time and end time of the operation from the production facility 2, and extracts the waveform data 31 from the sensor data 32.

[0049] Next, the feature quantity calculation unit 12 of the determination system 1 calculates a plurality of feature quantities 33 (step ST2). The feature quantity calculation unit 12, for example, acquires the start time and end time of each process during the operation of the production facility 2 from the production facility 2 and extracts partial waveforms from the waveform data 31. The feature quantity calculation unit 12 calculates a plurality of feature quantities 33 based on the partial waveforms. Note that the feature quantity calculation unit 12 may calculate one or more feature quantities 33 using a learned model that outputs one or more feature quantities 33 when, for example, the waveform data 31 or partial waveforms are input.

[0050] Next, the learning unit 14 of the determination system 1 generates a learned model. Specifically, the learning unit 14 generates a unit space 51 (step ST3). The learning unit 14 generates the unit space 51 based on, for example, a plurality of feature quantities 33 of each of the plurality of reference waveform data 41 stored in the storage unit 13.

[0051] Next, the determination unit 15 of the determination system 1 determines whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal (step ST4). The determination unit 15 calculates the degree of abnormality from multiple feature quantities 33 of the waveform data 31 using a trained model. For example, the determination unit 15 calculates the Mahalanobis distance d1 between the multiple feature quantities 33 of the waveform data 31 and the unit space 51 as the degree of abnormality. Based on the Mahalanobis distance d1, the determination unit 15 determines whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal.

[0052] Through the above operations, the judgment system 1 can determine whether the operation of the production equipment 2 is normal or abnormal based on the waveform data 31.

[0053] Here, the judgment system 1 may output the judgment result from the output unit 16. For example, if the output unit 16 determines that the operation of the production equipment 2 is abnormal, it outputs a stop command to the production equipment 2. This makes it possible to prevent a decrease in yield when a decrease in yield may occur due to an abnormality in the production equipment 2. Alternatively, for example, the output unit 16 displays an image on a display device that shows the judgment result to the operator of the production equipment 2. The image includes whether or not an abnormality has occurred in the operation of the production equipment 2, the frequency or tendency of the abnormality to occur, and candidate locations of the abnormality in the production equipment 2 that can be assumed from the frequency or tendency of the abnormality to occur. This makes it easy for the operator of the production equipment 2 to determine whether the abnormality in the production equipment 2 is a temporary problem or whether maintenance of the production equipment 2 is required.

[0054] Furthermore, the determination system 1 determines whether the operation of the production equipment 2 is normal or abnormal based on the similarity between the multiple reference waveform data 41 and the waveform data 31. Therefore, if the production equipment 2 and mold 21 equipped with the sensor 22 are the same for the multiple reference waveform data 41 and the waveform data 31, the determination system 1 can determine whether the operation of the production equipment 2 is normal or abnormal based on the waveform data 31. In other words, if the conditions for the operation of the production equipment 2 related to the waveform data 31 and the operation of the production equipment 2 related to the multiple reference waveform data 41 can be made the same, the determination system 1 can generally determine whether the operation of the production equipment 2 is normal or abnormal. Here, the condition for the operation of the production equipment 2 being the same means that the same waveform data 31 is obtained when the production equipment 2 performs the exact same operation.

[0055] (4) Effect The determination system 1 according to Embodiment 1 comprises an acquisition unit 11, a feature calculation unit 12, a learning unit 14, and a determination unit 15. The acquisition unit 11 acquires waveform data 31 from a sensor 22 installed on the production equipment 2. The feature calculation unit 12 calculates a plurality of feature quantities 33 from the waveform data 31. The learning unit 14 generates a trained model based on the plurality of feature quantities 33 of each of the plurality of reference waveform data 41. The plurality of reference waveform data 41 are acquired from the sensor 22. The determination unit 15 determines whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal based on the Mahalanobis distance d1, which is the degree of abnormality calculated from the plurality of feature quantities 33 of the waveform data 31. As a result, the determination system 1 according to Embodiment 1 makes it possible to determine an abnormal state in a general way based on the waveform data 31.

[0056] Furthermore, in the determination system 1 according to Embodiment 1, each of the multiple feature quantities 33 of the multiple reference waveform data 41 is a value that indicates whether the operation of the production equipment 2 corresponding to each of the multiple reference waveform data 41 is normal or abnormal. The multiple feature quantities 33 of the waveform data 31 and each of the multiple feature quantities 33 of the multiple reference waveform data 41 correspond to each other. When the learning unit 14 generates a trained model, it generates a unit space 51 based on each of the multiple feature quantities 33 of the multiple reference waveform data 41. As a result, according to the determination system 1 according to Embodiment 1, it is possible to determine with high accuracy whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal based on the similarity of the feature quantities 33 of the multiple reference waveform data 41 and the waveform data 31.

[0057] Furthermore, in the determination system 1 according to Embodiment 1, the determination unit 15 determines that the operation of the production equipment 2 corresponding to the waveform data 31 is abnormal if the Mahalanobis distance d1, which is the degree of abnormality, is greater than or equal to a threshold, and determines that the operation of the production equipment 2 corresponding to the waveform data 31 is normal if the Mahalanobis distance d1, which is the degree of abnormality, is less than a threshold. Thus, according to the determination system 1 according to Embodiment 1, it is possible to easily determine whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal based on its similarity to a plurality of reference waveform data 41.

[0058] Furthermore, in the determination system 1 according to Embodiment 1, the production equipment 2 is a mold press device. The sensor 22 acquires the distortion of the mold 21 included in the production equipment 2 as waveform data 31. As a result, according to the determination system 1 according to Embodiment 1, it is possible to detect abnormalities in the manufacturing process using a mold press device at an early stage.

[0059] Furthermore, in the determination system 1 according to Embodiment 1, the feature calculation unit 12 extracts one or more partial data 351 to 354 from the waveform data 31 and calculates multiple feature quantities 33 from the one or more partial data 351 to 354. Thus, according to the determination system 1 according to Embodiment 1, the feature calculation unit 12 can easily calculate multiple feature quantities 33 from the waveform data 31.

[0060] Furthermore, in the determination system 1 according to Embodiment 1, one or more partial data 351 to 354 are portions of the waveform data 31 whose waveform changes depending on whether the operation of the production equipment 2 is normal or abnormal. As a result, the determination system 1 according to Embodiment 1 can determine with high accuracy whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal.

[0061] Furthermore, in the determination system 1 according to Embodiment 1, the feature calculation unit 12 calculates the values ​​included in the partial data, or the values ​​calculated from the values ​​included in the partial data, from each of the one or more partial data 351 to 354 as the multiple feature quantities. As a result, the determination system 1 according to Embodiment 1 improves the accuracy of determining whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal, and makes it possible to easily calculate the multiple feature quantities 33.

[0062] Furthermore, in the determination system 1 according to Embodiment 1, an abnormality in the operation of the production equipment 2 is defined as waste buildup. As a result, according to the determination system 1 according to Embodiment 1, it is possible to detect early on situations in which products manufactured by the production equipment 2 become defective.

[0063] Furthermore, the determination system 1 according to Embodiment 1 further includes an output unit 16. The output unit 16 outputs a result indicating whether the operation of the production equipment 2 corresponding to the waveform data 31 determined by the determination unit 15 is normal or abnormal. As a result, according to the determination system 1 of Embodiment 1, for example, the operator of the production equipment 2 can take early action against any abnormalities in the production equipment 2.

[0064] Furthermore, in the determination system 1 according to Embodiment 1, the output unit 16 outputs the result as an image. As a result, according to the determination system 1 according to Embodiment 1, the operator of the production equipment 2 can easily determine whether the abnormality of the production equipment 2 is a temporary problem or whether the production equipment 2 requires maintenance.

[0065] Furthermore, the determination system 1 according to Embodiment 1 further includes a storage unit 13. The storage unit 13 stores the results output by the output unit 16, waveform data 31, and a plurality of feature quantities 33 calculated from the waveform data 31. As a result, according to the determination system 1 of Embodiment 1, the operator of the production equipment 2 can make a more accurate judgment about the state of the production equipment 2.

[0066] Furthermore, the determination method according to Embodiment 1 includes an acquisition step ST1, a feature calculation step ST2, and a determination step ST4. In the acquisition step ST1, waveform data 31 is acquired from a sensor 22 installed on the production equipment 2. In the feature calculation step ST2, a plurality of feature quantities 33 are calculated from the waveform data 31. In the determination step ST4, a trained model generated based on a plurality of reference waveform data 41 is used to determine whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal, based on the degree of abnormality calculated from the plurality of feature quantities 33 of the waveform data 31. The plurality of reference waveform data 41 are acquired from the sensor 22. The program according to Embodiment 1 causes one or more processors to execute the determination method according to Embodiment 1. As a result, according to the determination method and program according to Embodiment 1, it is possible to determine an abnormal state in a general manner based on the waveform data 31.

[0067] Furthermore, the determination method according to Embodiment 1 includes a learning step ST3. In the learning step ST3, a trained model is generated based on multiple feature quantities 33 of each of the multiple reference waveform data 41. As a result, according to the determination method according to Embodiment 1, it is possible to determine with high accuracy whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal, based on the similarity of the feature quantities 33 between the reference waveform data 41 and the waveform data 31.

[0068] (Embodiment 2) (1) Configuration The determination system 1 (see Figure 1) according to embodiment 2 is a system that determines whether the operation of the production equipment 2 is in an abnormal state based on waveform data 31 (see Figure 3) that shows one of a plurality of operations that are repeatedly performed in the production equipment 2 (see Figure 6). The production equipment 2 is, for example, a die press machine that uses a die 21. Specifically, an abnormal state in the operation of the production equipment 2 refers to a defect in the die 21 due to debris clogging. Debris clogging refers to the clogging of the through hole 211 of the die 21 by debris 24 during the hole-punching process using the die 21. Causes of debris clogging include the clogging 24 sticking to the inner wall of the through hole 251 of the back plate 25 due to machine oil, and the clogging 24 getting caught in the through hole 211 of the die 21 or the through hole 251 of the back plate 25 due to burrs being generated on the outer circumference of the clogging 24 during the hole-punching process. A clogging defect refers to a situation where the material 23 cannot be processed properly due to the presence of debris 24 in the through-hole 211 of the mold 21.

[0069] The feature calculation unit 12 calculates multiple feature quantities 33 from the waveform data 31. For example, the feature calculation unit 12 obtains the operating timing from the production equipment 2 and extracts partial data from the waveform data 31.

[0070] The partial data includes, for example, waveform 352a or waveform 352b from the waveform data 31, corresponding to the punching process and the bottom dead center process, as shown in Figure 7. In Figure 7, waveform 352a is the waveform when a debris clogging defect occurs, and waveform 352b is the waveform when a debris clogging defect does not occur. The feature calculation unit 12 detects, for example, the time when the strain is at its minimum immediately after the upper die punch punches out the material 23, as a reference point, and extracts waveform 352a or waveform 352b as partial data based on the reference point.

[0071] One of the multiple feature quantities 33 is, for example, as shown in Figure 7, the difference da1 and db1 between the maximum distortion value and the minimum distortion value of the waveforms 352a and 352b. When a slag jamming defect occurs, the jammed slag 24 obstructs the movement of the punch in the upper die, so the feature quantity da1 when slag jamming occurs is larger than the feature quantity db1 when slag jamming does not occur.

[0072] Furthermore, one of the multiple feature quantities 33 is, for example, as shown in Figure 7, the difference da2 and db2 between the maximum distortion values ​​of waveforms 352a and 352b and the minimum values ​​after time t21 when the distortion reached its maximum value. When no slag buildup occurs, the slag 24 completely detaches from the mold 21, causing the distortion of the mold 21 to change before and after the upper mold punch reaches its lowest point. In contrast, when slag buildup occurs, the slag 24 remains in contact with the mold 21, so the change in the distortion of the mold 21 before and after the upper mold punch reaches its lowest point is small. Therefore, the feature quantity da2 when slag buildup occurs is smaller than the feature quantity db2 when slag buildup does not occur.

[0073] Therefore, even if the abnormality in the operation of the production equipment 2 is due to a blockage of waste, the judgment system 1 can determine whether the operation of the production equipment 2 is normal or abnormal based on the waveform data 31.

[0074] (2) Effects In the determination system 1 according to Embodiment 2, the abnormality in the operation of the production equipment 2 is a blockage of waste. As a result, according to the determination system 1 according to Embodiment 2, it is possible to detect at an early stage when the products manufactured by the production equipment 2 become defective.

[0075] (Embodiment 3) (1) Configuration In the determination system 1 according to embodiment 1, partial data is extracted based on the start time and end time of each process within the operation of the production equipment 2. In contrast, the determination system 1 according to embodiment 3 extracts partial data based on the waveform characteristics of the waveform data 31.

[0076] The feature calculation unit 12 extracts partial data based on reference points detected based on the waveform of the waveform data. The feature calculation unit 12 detects the reference points based on the waveform of the waveform data.

[0077] Specifically, the feature calculation unit 12 detects the strain peak (see Figure 3) generated when the stripper presses the material 23 in the production equipment 2 as a reference point. The feature calculation unit 12 extracts partial data from the waveform data 31 based on the reference point. The feature calculation unit 12 may extract partial data so as to include the reference point, or it may extract partial data after or before the reference point. For example, the feature calculation unit 12 extracts waveform 352 as partial data so as to include the reference point, and extracts partial data that is earlier in time than waveform 352 as the initial waveform section 351. In addition, the feature calculation unit 12 detects the strain peak (see Figure 3) generated when the upper die punch punches out the material 23 in the production equipment 2 as a reference point, and extracts waveform 353 as partial data based on the reference point. Furthermore, the feature calculation unit 12 detects the peak of strain that occurs when the stripper is lowered completely in the production equipment 2 as a reference point, and extracts the waveform 354 as partial data based on the reference point.

[0078] The feature calculation unit 12 extracts partial data from each of the waveform data 31 using the above-described process, and extracts multiple feature quantities 33. This makes it possible to determine whether the operation of the production equipment 2 is normal or abnormal, even if the time required for each process is not constant in each operation of the production equipment 2.

[0079] Figure 8 is a flowchart detailing the operation of step ST3 (see Figure 5), in which the learning unit 14 of the judgment system 1 generates a unit space 51. The feature calculation unit 12 searches for a reference point for each of the waveform data 31 (step S31). Next, the feature calculation unit 12 extracts the partial data using the reference point as the reference position (step S32) and extracts multiple feature quantities 33 (step S33). The learning unit 14 generates a unit space 51 based on the multiple feature quantities 33 obtained by repeating steps S31 to S33.

[0080] Figure 9 is a flowchart detailing the operation of step ST4 (see Figure 5), in which the determination unit 15 of the determination system 1 determines whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal. The feature calculation unit 12 searches for a reference point for partial data in the waveform data 31 (step S41). Next, the feature calculation unit 12 extracts partial data using the reference point as the reference position (step S42) and extracts multiple feature quantities 33 (step S43). The determination unit 15 calculates the degree of abnormality based on the multiple feature quantities 33 of the waveform data 31 and the unit space 51 (step S44).

[0081] (2) Effects In the determination system 1 according to Embodiment 3, the feature calculation unit 12 extracts one or more partial data based on reference points detected based on the waveform of the waveform data. As a result, according to the determination system 1 according to Embodiment 1, even if there is a time lag between multiple operations repeated in the production equipment 2, the accuracy of the feature quantities is improved, making it possible to determine with high accuracy whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal.

[0082] Furthermore, in the determination system 1 according to Embodiment 3, the feature calculation unit 12 detects reference points based on the waveform of the waveform data 31. As a result, the determination system 1 according to Embodiment 1 can determine with high accuracy whether the operation of the production equipment 2 corresponding to the waveform data 31 is normal or abnormal.

[0083] (Other modifications according to the embodiment) (1) The determination system 1 according to embodiments 1 and 2 includes a learning unit 14, but the determination system 1 may acquire a unit space 51 generated by another device or another system, for example.

[0084] Furthermore, in the determination system 1 according to Embodiments 1 and 2, a unit space 51 is generated each time an operation is performed (step ST3). However, if the multiple reference waveform data 41 are the same, a unit space 51 that has already been generated based on the multiple reference waveform data 41 may be used.

[0085] (2) The determination system 1 according to Embodiment 1 detects sludge rising, and the determination system 1 according to Embodiment 2 detects sludge clogging, but the determination system 1 may detect both sludge rising and sludge clogging using a single unit space 51.

[0086] (Aspect) The determination system (1) according to the first aspect comprises an acquisition unit (11), a feature calculation unit (12), a learning unit (14), and a determination unit (15). The acquisition unit (11) acquires waveform data (31) from a sensor (22) installed in the production equipment (2). The feature calculation unit (12) calculates a plurality of features (33) from the waveform data (31). The learning unit (14) generates a trained model based on the plurality of features (33) of each of the plurality of reference waveform data (41). The plurality of reference waveform data (41) are acquired from the sensor (22). The determination unit (15) determines whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal based on the degree of abnormality (Mahalanobis distance d1) calculated from the plurality of features (33) of the waveform data (31) using the trained model.

[0087] According to the judgment system (1) described above, it is possible to determine an abnormal state in a general manner based on waveform data (31).

[0088] In the second embodiment of the determination system (1), in the first embodiment, each of the multiple feature quantities (33) of the multiple reference waveform data (41) is a value that indicates whether the operation of the production equipment (2) corresponding to each of the multiple reference waveform data (41) is normal or abnormal. The multiple feature quantities (33) of the waveform data (31) and each of the multiple feature quantities (33) of the multiple reference waveform data (41) correspond to each other. When the learning unit (14) generates a trained model, it generates a unit space (51) based on each of the multiple feature quantities (33) of the multiple reference waveform data (41).

[0089] According to the judgment system (1) described above, it is possible to determine with high accuracy whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal, based on the similarity of the feature quantities (33) between a plurality of reference waveform data (41) and the waveform data (31).

[0090] In the third embodiment of the determination system (1), in the second embodiment, the determination unit (15) determines that the operation of the production equipment (2) corresponding to the waveform data (31) is abnormal if the degree of abnormality (Mahalanobis distance d1) is greater than or equal to a threshold. The determination unit (15) determines that the operation of the production equipment (2) corresponding to the waveform data (31) is normal if the degree of abnormality (Mahalanobis distance d1) is less than a threshold.

[0091] According to the determination system (1) described above, it is possible to easily determine whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal based on the similarity between the waveform data (31) and a plurality of reference waveform data (41).

[0092] In the determination system (1) relating to the fourth embodiment, in any of the first to third embodiments, the production equipment (2) is a mold press machine. The sensor (22) acquires the distortion of the mold (21) included in the production equipment (2) as waveform data (31).

[0093] According to the judgment system (1) described above, it becomes possible to detect abnormalities in the manufacturing process using a mold press device at an early stage.

[0094] In the determination system (1) according to the fifth embodiment, in any of the first to fourth embodiments, the feature calculation unit (12) extracts one or more partial data (351 to 354) from the waveform data (31) and calculates multiple features (33) from one or more partial data (351 to 354).

[0095] According to the determination system (1) described above, the feature calculation unit (12) can easily calculate multiple feature quantities (33) from the waveform data (31).

[0096] In the determination system (1) according to the sixth embodiment, in the fifth embodiment, one or more partial data (351 to 354) are the parts of the waveform data (31) whose waveform changes depending on whether the operation of the production equipment (2) is normal or abnormal.

[0097] According to the judgment system (1) described above, it is possible to determine with high accuracy whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal.

[0098] In the seventh aspect of the determination system (1), in the fifth or sixth aspect, the feature calculation unit (12) calculates from each of one or more partial data (351 to 354) values ​​contained in the partial data, or values ​​calculated from the values ​​contained in the partial data, as a plurality of features (33).

[0099] According to the judgment system (1) described above, the accuracy of determining whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal is improved, and it becomes possible to easily calculate multiple feature quantities (33).

[0100] In the eighth aspect of the determination system (1), in any of the fifth to seventh aspects, the feature calculation unit (12) extracts one or more sub-data (351 to 354) based on reference points detected based on the waveform of the waveform data (31).

[0101] According to the judgment system (1) described above, even if there is a time lag between multiple operations repeated in the production equipment (2), the accuracy of the feature quantities is improved, making it possible to determine with high accuracy whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal.

[0102] In the determination system (1) according to the ninth embodiment, in the eighth embodiment, the feature calculation unit (12) detects the reference point based on the waveform of the waveform data (31).

[0103] According to the judgment system (1) described above, it is possible to determine with high accuracy whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal.

[0104] In the determination system (1) relating to the tenth embodiment, in any of the fourth to ninth embodiments, the abnormality in the operation of the production equipment (2) is either sludge buildup or sludge clogging.

[0105] According to the judgment system (1) described above, it is possible to detect early on when a product manufactured by the production equipment (2) becomes a defective product.

[0106] The determination system (1) according to the eleventh embodiment further comprises an output unit (16) in any of the first to tenth embodiments. The output unit (16) outputs a result indicating whether the operation of the production equipment (2) corresponding to the waveform data (31) determined by the determination unit (15) is normal or abnormal.

[0107] According to the judgment system (1) described above, for example, an operator of the production equipment (2) can take early action against any abnormalities in the production equipment (2).

[0108] In the determination system (1) according to the twelfth embodiment, in the eleventh embodiment, the output unit (16) outputs the result as an image.

[0109] According to the determination system (1) described above, the operator of the production equipment (2) can easily determine whether the abnormality of the production equipment (2) is a temporary problem or whether the production equipment (2) requires maintenance.

[0110] In the determination system (1) according to the 13th embodiment, a memory unit (13) is further provided in the 11th or 12th embodiment. The memory unit (13) stores the result output by the output unit (16), waveform data (31), and a plurality of feature quantities (33) calculated from the waveform data (31).

[0111] According to the judgment system (1) described above, the operator of the production equipment (2) will be able to make a more accurate judgment about the state of the production equipment (2).

[0112] The 14th aspect of the determination method includes an acquisition step (ST1), a feature calculation step (ST2), and a determination step (ST4). In the acquisition step (ST1), waveform data (31) is acquired from a sensor (22) installed on the production equipment (2). In the feature calculation step (ST2), multiple features (33) are calculated from the waveform data (31). In the determination step (ST4), a trained model generated based on multiple reference waveform data (41) is used to determine whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal, based on the degree of abnormality (Mahalanobis distance d1) calculated from the multiple features (33) of the waveform data (31). The multiple reference waveform data (41) are acquired from the sensor (22).

[0113] According to the determination method described above, it becomes possible to determine an abnormal state in a general manner based on waveform data (31).

[0114] The determination method according to the 15th embodiment further includes a learning step (ST3) in the 14th embodiment. In the learning step (ST3), a trained model is generated based on multiple feature quantities (33) of each of the multiple reference waveform data (41).

[0115] According to the determination method described above, it is possible to determine with high accuracy whether the operation of the production equipment (2) corresponding to the waveform data (31) is normal or abnormal, based on the similarity of the feature quantities (33) between a plurality of reference waveform data (41) and the waveform data (31).

[0116] The program according to the 16th embodiment causes one or more processors to execute the determination method according to the 14th or 15th embodiment.

[0117] According to the program described above, it becomes possible to determine an abnormal state in a general manner based on waveform data (31).

[0118] 1 Judgment System 11 Acquisition Unit 12 Feature Calculation Unit 13 Memory Unit 14 Learning Unit 15 Judgment Unit 16 Output Unit 2 Production Equipment 21 Mold 22 Sensor 31 Waveform Data 33 Feature Quantity 351 Initial Waveform (Partial Data) 352 Waveform Corresponding to Stripper Process (Partial Data) 353 Waveform Corresponding to Punching Process (Partial Data) 354 Waveform Corresponding to Bottom Dead Center Process (Partial Data) 41 Reference Waveform Data 51 Unit Space ST1 Acquisition Step ST2 Feature Calculation Step ST3 Learning Step ST4 Judgment Step d1 Mahalanobis Distance (Anomaly Score)

Claims

1. A determination system comprising: an acquisition unit that acquires waveform data from a sensor installed on production equipment; a feature calculation unit that calculates a plurality of feature quantities from the waveform data; a learning unit that generates a trained model based on the plurality of feature quantities of each of the plurality of reference waveform data acquired from the sensor; and a determination unit that determines whether the operation of the production equipment corresponding to the waveform data is normal or abnormal based on the degree of abnormality calculated from the plurality of feature quantities of the waveform data using the trained model.

2. The determination system according to claim 1, wherein each of the plurality of feature quantities of the plurality of reference waveform data is a value indicating whether the operation of the production equipment corresponding to each of the plurality of reference waveform data is normal or abnormal, the plurality of feature quantities of the waveform data and each of the plurality of feature quantities of the plurality of reference waveform data correspond to each other, and the learning unit generates a unit space based on each of the plurality of feature quantities of the plurality of reference waveform data when generating the trained model.

3. The determination system according to claim 2, wherein the determination unit determines that the operation of the production equipment corresponding to the waveform data is abnormal if the degree of abnormality is greater than or equal to a threshold, and determines that the operation of the production equipment corresponding to the waveform data is normal if the degree of abnormality is less than the threshold.

4. The determination system according to any one of claims 1 to 3, wherein the production equipment is a mold press machine, and the sensor acquires the distortion of a mold included in the production equipment as waveform data.

5. The determination system according to any one of claims 1 to 4, wherein the feature calculation unit extracts one or more partial data from the waveform data and calculates the plurality of feature quantities from the one or more partial data.

6. The determination system according to claim 5, wherein the one or more of the aforementioned partial data are portions of the waveform data in which the waveform changes depending on whether the operation of the production equipment is normal or abnormal.

7. The determination system according to claim 5 or 6, wherein the feature calculation unit calculates from each of the one or more partial data values ​​a value included in the partial data, or a value calculated from the value included in the partial data, as the plurality of features.

8. The determination system according to any one of claims 5 to 7, wherein the feature calculation unit extracts one or more of the partial data based on reference points detected based on the waveform of the waveform data.

9. The determination system according to claim 8, wherein the feature calculation unit detects the reference point based on the waveform of the waveform data.

10. The determination system according to any one of claims 4 to 9, wherein the abnormality in the operation of the production equipment is due to sludge buildup or sludge clogging.

11. The determination system according to any one of claims 1 to 10, further comprising an output unit that outputs a result indicating whether the operation of the production equipment corresponding to the waveform data determined by the determination unit is normal or abnormal.

12. The determination system according to claim 11, wherein the output unit outputs the result as an image.

13. The determination system according to claim 11 or 12, further comprising a storage unit that stores the result output by the output unit, the waveform data, and the plurality of feature quantities calculated from the waveform data.

14. A determination method comprising: an acquisition step of acquiring waveform data from a sensor installed on production equipment; a feature calculation step of calculating a plurality of feature quantities from the waveform data; and a determination step of determining whether the operation of the production equipment corresponding to the waveform data is normal or abnormal based on an abnormality score calculated from the plurality of feature quantities of the waveform data using a trained model generated based on a plurality of reference waveform data acquired from the sensor.

15. The determination method according to claim 14, further comprising a learning step of generating the trained model based on a plurality of features of each of the plurality of reference waveform data.

16. A program that causes one or more processors to execute the determination method described in claim 14 or 15.

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