Anomaly detection device, anomaly detection method, and program

Abstract

To detect anomalies with greater accuracy. [Solution] The system includes: a data acquisition unit that acquires abnormal time-series data, which is time-series data of an abnormal state of a rotating machine; a generation unit that corrects the abnormal time-series data to generate multiple simulated data, which is time-series data that simulates the abnormality; a learning unit that uses the simulated data and labels indicating the state of the simulated data as training data to train a learning model and generate a trained model; a determination unit that inputs target time-series data, which is time-series data targeted by the rotating machine, into the trained model and performs abnormality determination of the target time-series data; and an output control unit that outputs the result of the abnormality determination of the target time-series data.

Description

[Technical Field]

[0001] The present disclosure relates to an abnormality determination apparatus, an abnormality determination method, and a program. [Background Art]

[0002] Technologies for identifying abnormality causes of machines are known. For example, Patent Document 1 describes that past data of a rotating machine is accumulated, simulation data is generated from the past data, machine learning is performed using the accumulated past data and the simulation data, and an abnormality cause of the rotating machine is identified using learned data. [Prior Art Documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Unexamined Patent Publication No. 2020-64024 [Summary of the Invention] [Problem to be Solved by the Invention]

[0004] When determining an abnormality of a rotating machine using learning data, there is a demand for more accurate abnormality determination.

[0005] An object of the present disclosure is to provide an abnormality determination apparatus, an abnormality determination method, and a program that can determine an abnormality with higher accuracy. [Means for Solving the Problem]

[0006] The abnormality detection device according to this disclosure includes: a data acquisition unit that acquires abnormal time-series data, which is time-series data of an abnormal state of a rotating machine; a generation unit that corrects the abnormal time-series data to generate a plurality of simulated data, which is time-series data that simulates an abnormality; a learning unit that uses the simulated data and labels indicating the state of the simulated data as training data to train a learning model and generate a trained model; a determination unit that inputs target time-series data, which is time-series data targeted by the rotating machine, into the trained model and performs abnormality detection on the target time-series data; and an output control unit that outputs the result of the abnormality detection on the target time-series data.

[0007] The abnormality detection method relating to this disclosure includes the steps of: acquiring abnormal time series data, which is time series data of an abnormal state of a rotating machine; correcting the abnormal time series data to generate a plurality of simulated data, which is time series data that simulates an abnormality; training a learning model using the simulated data and labels indicating the state of the simulated data as training data to generate a trained model; inputting target time series data, which is time series data targeted by the rotating machine, into the trained model to perform abnormality detection on the target time series data; and outputting the result of the abnormality detection on the target time series data.

[0008] The program relating to this disclosure causes a computer to perform the following steps: acquire abnormal time series data, which is time series data of an abnormal state of a rotating machine; correct the abnormal time series data to generate multiple simulated data, which is time series data that simulates the abnormality; train a learning model using the simulated data and labels indicating the state of the simulated data as training data to generate a trained model; input target time series data, which is time series data of the rotating machine, into the trained model to perform abnormality determination of the target time series data; and output the result of the abnormality determination of the target time series data. [Effects of the Invention]

[0009] According to this disclosure, it is possible to determine anomalies with greater accuracy. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is a block diagram showing the abnormality detection device of this embodiment. [Figure 2] Figure 2 shows an example of simulated data. [Figure 3] Figure 3 shows an example of simulated data. [Figure 4] Figure 4 shows an example of simulated data. [Figure 5] Figure 5 shows an example of simulated data. [Figure 6] Figure 6 is a flowchart showing an example of the processing performed by the anomaly detection device. [Modes for carrying out the invention]

[0011] Preferred embodiments of the present disclosure will be described in detail below with reference to the attached drawings. However, this disclosure is not limited to these embodiments, and if there are multiple embodiments, they may be combinations of these embodiments.

[0012] (Abnormality determination device) Figure 1 is a block diagram showing the abnormality detection device of this embodiment. The abnormality detection device 10 according to this embodiment generates multiple simulated data from time-series data of a rotating machine, trains a learning model with the generated simulated data, and generates a trained model. The abnormality detection device 10 then inputs the target time-series data of the rotating machine to be abnormal into the trained model, determines the abnormality of the rotating machine, and outputs the determination result. The rotating machine may be any machine that performs work by rotation, such as a compressor or a turbine. The abnormality detection device 10 may also determine abnormalities in the entire rotating machine or in parts mounted on the rotating machine. Examples of parts mounted on a rotating machine include shafts and bearings.

[0013] Furthermore, the term "time-series data" here refers to data detectable from rotating machinery, whose values ​​can change over time. Time-series data can also be described as data indicating the state of the rotating machinery, detected by sensors installed on the machinery. Examples of time-series data include data on the shaft vibration of the rotating machinery and data on the temperature of the bearings of the rotating machinery. For example, the sensor used to detect time-series data may be a vibration sensor, and the time-series data detected by that sensor will be the shaft vibration values ​​detected for each time series. Alternatively, the sensor used to detect time-series data may be a thermometer, and the time-series data detected by that sensor will be the bearing temperatures detected for each time series.

[0014] The abnormality detection device 10 according to this embodiment is, for example, a computer and, as shown in Figure 1, has an input unit 12, an output unit 14, a communication unit 16, a storage unit 18, and a control unit 20. The input unit 12 is a device that accepts user input and may be, for example, a mouse, keyboard, or touch panel. The output unit 14 is a device that outputs information and may be, for example, a display that shows images. The communication unit 16 is a module that communicates with external devices and may include, for example, an antenna. In this embodiment, the communication method used by the communication unit 16 is wireless communication, but the communication method may be arbitrary. The abnormality detection device 10 does not necessarily have an input unit 12, an output unit 14, or a communication unit 16. Furthermore, the abnormality detection device 10 may be configured as a standalone device, integrated with other devices, or as a system combining various devices such as a computing unit and a data server, and is not particularly limited.

[0015] The memory unit 18 is a memory that stores various information such as the calculation contents and programs of the control unit 20, and includes at least one of the following: RAM (Random Access Memory), main memory such as ROM (Read Only Memory), and external memory such as HDD (Hard Disk Drive). The program for the control unit 20 stored in the memory unit 18 may be stored on a recording medium that can be read by the abnormality detection device 10.

[0016] The control unit 20 is an arithmetic device and includes an arithmetic circuit such as a CPU (Central Processing Unit), for example. The control unit 20 includes a data acquisition unit 30, a preprocessing unit 32, a generation unit 34, a learning unit 36, a determination unit 38, and an output control unit 40. The control unit 20 reads and executes a program (software) from the storage unit 18, thereby implementing the data acquisition unit 30 and the output control unit 40 and executing processes thereof. Note that the control unit 20 may execute these processes by a single CPU, or may include a plurality of CPUs and execute the processes by the plurality of CPUs. Furthermore, at least part of the data acquisition unit 30, the preprocessing unit 32, the generation unit 34, the learning unit 36, the determination unit 38, and the output control unit 40 may be implemented by hardware.

[0017] Hereinafter, the processing contents of the abnormality determination device 10 will be described.

[0018] (Acquisition of abnormal time-series data) The data acquisition unit 30 acquires abnormal time-series data, which is time-series data in an abnormal state of a rotating machine. Abnormal time-series data refers to time-series data of the rotating machine that is detected during a period in which the rotating machine was in an abnormal state. The data acquisition unit 30 may acquire abnormal time-series data by any method; for example, the data acquisition unit 30 may acquire abnormal time-series data from a sensor via the communication unit 16, or may acquire abnormal time-series data when an operator inputs the abnormal time-series data to the input unit 12. The data acquisition unit 30 may also similarly acquire normal time-series data, which is time-series data in a normal state of the rotating machine. Normal time-series data refers to time-series data of the rotating machine that is detected during a period in which the rotating machine was in a normal state.

[0019] In addition, the data acquisition unit 30 may also acquire, together with the abnormal time-series data, a label indicating the state of the abnormal time-series data (the state of the rotating machine during the period in which the abnormal time-series data is detected). Since the abnormal time-series data is time-series data in an abnormal state, the label of the abnormal time-series data indicates that the abnormal time-series data (the rotating machine in the period when the abnormal time-series data is detected) is abnormal. However, the label is not limited to only indicating that the state is abnormal; in the present embodiment, it is information indicating an abnormality mode indicating what kind of abnormality the time-series data has. Specifically, the label of the abnormal time-series data in the present embodiment may be data indicating the waveform shape of the abnormal time-series data, for example, it may be at least one of that the waveform of the time-series data is spiky, that the time-series data continues to increase over time, and that the time-series data continues to decrease over time. That the waveform is spiky means that the time-series data includes a waveform having a high peak value (for example, equal to or greater than a predetermined value) and a small time width of the peak mountain (for example, equal to or less than a predetermined time). In addition, that the time-series data continues to increase over time means that the time width during which the time-series data continues to increase as time elapses is long (for example, equal to or greater than a predetermined time). In addition, that the time-series data continues to decrease over time means that the time width during which the time-series data continues to decrease as time elapses is long (for example, equal to or greater than a predetermined time). The data acquisition unit 30 may acquire the label of the abnormal time-series data by any method. For example, the label may be acquired when an operator inputs the label into the input unit 12.

[0020] It should be noted that, when the data acquisition unit 30 also acquires normal time-series data, it may also acquire a label for the normal time-series data. The label for the normal time-series data may indicate that the normal time-series data (the rotating machine during the period in which the normal time-series data is detected) is normal.

[0021] In addition, the data acquisition unit 30 may also acquire correction information (described later) for correcting the abnormal time-series data.

[0022] (Pre-processing) The preprocessing unit 32 performs preprocessing on the abnormal time-series data acquired by the data acquisition unit 30. Preprocessing here refers to extraction, normalization, and filtering.

[0023] The preprocessing unit 32 performs an extraction process to extract abnormal time series data from the acquired abnormal time series data within a predetermined time range. The predetermined time range is, for example, the time range in which the learning model can learn normally.

[0024] Furthermore, the preprocessing unit 32 performs a normalization process on the abnormal time series data. For example, the preprocessing unit 32 performs a normalization process on the abnormal time series data extracted in the extraction process. Normalization refers to the process of normalizing the values ​​of the abnormal time series data (for example, the values ​​of axial vibration) so that they fall within a predetermined numerical range. For example, the preprocessing unit 32 may normalize the values ​​of each time series of the abnormal time series data so that the mean is 0 and the deviation is 1. An example of a normalization process is to subtract the mean from the original data and divide by the standard deviation.

[0025] The preprocessing unit 32 performs filtering on the abnormal time series data. The preprocessing unit 32 may perform filtering on the acquired abnormal time series data before performing normalization, or it may perform filtering on the abnormal time series data after normalization. Filtering refers to the process of removing waveforms from the abnormal time series data in which the amplitude of the peak intensity is less than a threshold.

[0026] Furthermore, if the data acquisition unit 30 has also acquired normal time series data, the preprocessing unit 32 may preprocess the normal time series data in the same manner as the abnormal time series data.

[0027] In the above, extraction, normalization, and filtering were performed as preprocessing steps, but it is also possible to perform at least one of these steps. Furthermore, preprocessing is not mandatory, and it may be omitted.

[0028] (Generating simulated data) Figures 2 to 5 show examples of simulated data. Line D in Figures 2 to 5 represents an example of simulated data. In Figures 2 to 5, the horizontal axis represents time, and the vertical axis represents the values ​​of the simulated data (e.g., vibration values).

[0029] The generation unit 34 corrects the acquired abnormal time series data and generates multiple simulated data sets, which are time series data that simulate the abnormality. In other words, the simulated data is not the time series data actually detected by the rotating machine's sensor, but rather time series data that simulates the time series data in an abnormal state, generated by correcting the abnormal time series data. In this embodiment, since the abnormal time series data is preprocessed, the generation unit 34 generates the simulated data based on the preprocessed abnormal time series data, but it is not limited to this, and may also generate simulated data based on abnormal time series data that has not been preprocessed.

[0030] The generation unit 34 uses abnormal time series data (for example, abnormal time series data about the shaft vibration of a rotating machine) to generate simulated data of the same type as the abnormal time series data, but with different waveforms (for example, time series data about the shaft vibration of a rotating machine, but with different waveforms). The generation unit 34 may generate the simulated data using any method with the abnormal time series data, but in this embodiment, the generation unit 34 generates the simulated data by correcting the abnormal time series data based on correction information. The correction information is information for generating the simulated data, or in other words, information that indicates which features of the abnormal time series data should be changed. The correction information is input (specified) by an operator, for example. The correction information, i.e., the features to be changed, may be at least one of the following: the period of the time series data, the timing when the time series data peaks, the number of peaks included in the time series data, and the intensity of the peaks included in the time series data.

[0031] In other words, the generation unit 34 of this embodiment generates a plurality of simulated data by changing at least one of the period, peak timing, number of peaks, and peak intensity of the acquired abnormal time series data. For example, if the correction information specifies a period T, time t, number of peaks n, and peak intensity H, the generation unit 34 generates n peaks with a period T, time t, and peak intensity H. The correction information may have a range set for time t. For example, if the correction information specifies a period T, time t x ~t y Given the number of peaks n and the peak intensity H, a time t with period T is generated. x ~t y At this point in time, n peaks are generated with peak intensity H.

[0032] Furthermore, the generation unit 34 sets labels for the generated simulated data that indicate the abnormal state of the simulated data. That is, the generation unit 34 generates multiple datasets of simulated data and labels. The generation unit 34 may set labels for the simulated data arbitrarily, but for example, it may set the label associated with the abnormal time series data used to generate the simulated data as the label for the simulated data. That is, for example, if there is abnormal time series data that has been labeled by the data acquisition unit 30 as having a spike-like waveform, the label for the simulated data generated based on that abnormal time series data will be the label indicating that the waveform is spike-like.

[0033] Furthermore, if the data acquisition unit 30 has also acquired normal time series data, the generation unit 34 may generate normal simulated data, which is time series data that simulates normal conditions, based on the normal time series data. In this case, the method for generating normal simulated data based on normal time series data is the same as the method for generating simulated data based on abnormal time series data, so the explanation will be omitted. The generation unit 34 will set a label indicating that the normal simulated data is normal.

[0034] Figure 2 shows an example of normal simulated data. As shown in Figure 2, the normal simulated data is generated based on normal time series data and is labeled as normal.

[0035] Figure 3 shows an example of simulated data for an anomaly (a). An anomaly (a) refers to an abnormal state in which the time-series data continues to decline over time. As shown in Figure 3, the simulated data for an anomaly (a) shows that the value continues to decline over time and is labeled as an anomaly (a) (a label indicating that the value continues to decline over time).

[0036] Figure 4 shows an example of simulated data for an anomaly (b). An anomaly (b) refers to an abnormal state where the time-series data continues to rise over time. As shown in Figure 4, the simulated data for an anomaly (b) shows that the value continues to rise over time and is labeled as an anomaly (b) (a label indicating that the value continues to rise over time).

[0037] Figure 5 shows an example of simulated data for anomaly (c). Anomaly (c) indicates an abnormal state in which the time-series data contains spike-like waveforms. As shown in Figure 5, the simulated data for anomaly (c) contains spike-like waveforms and is labeled as anomaly (c) (a label indicating the presence of spike-like waveforms). Figure 5(a) shows the case with one spike-like waveform, and Figure 5(b) shows the case with multiple spike-like waveforms.

[0038] The labels associated with the simulated data may be in any format indicating abnormal or normal conditions, but they may also be defined by numerical values, for example. That is, for example, the generation unit 34 may associate a label of 0 with normal simulated data generated by correcting normal time series data. The generation unit 34 may also associate a label of a value other than 0 with simulated data generated by correcting abnormal time series data. In this case, for example, different numerical values ​​may be assigned to different types of labels associated with abnormal simulated data. For example, the generation unit 34 may associate a label of 1 with simulated data showing a downward trend, a label of 2 with simulated data showing an upward trend, and a label of 3 with simulated data containing spike-like waveforms.

[0039] Furthermore, it is preferable that the generation unit 34 generates simulated data by adding random noise to abnormal time series data. When the generation unit 34 generates normal simulated data from normal time series data, it is preferable not to add random noise.

[0040] (Generation of a learning model) The learning unit 36 ​​trains a pre-training learning model to generate a machine-learned learning model that recognizes the correspondence between time-series data and the presence or absence of anomalies in the rotating machinery (time-series data). The learning unit 36 ​​uses simulated data and the labels associated with that simulated data as training data to train the learning model and generate a trained model. The learning model may be any AI model, but it is a learning model trained by deep learning, and consists of a model that defines the neural network constituting the classifier trained by deep learning (neural network configuration information) and variables. The learning model N may be capable of determining the label of the data based on the input data. In the example of this embodiment, the learning model N is an RNN (Recurrent Neural Network) model, but it is not limited to an RNN model and may be any type of learning model. An RNN is a neural network that has an input layer, an intermediate layer and an output layer, and has a structure in which the output of the intermediate layer (forward propagation) is recursively input to the intermediate layer (backpropagation).

[0041] The learning unit 36 ​​takes simulated data as input values ​​and outputs the labels of the input simulated data, and inputs this dataset as training data into the learning model. The learning unit 36 ​​prepares multiple datasets of simulated data and labels, and inputs each of the multiple datasets into the learning model. In other words, in this embodiment, since multiple sets of simulated data with different waveforms are generated, the learning model can be trained with high accuracy by using multiple sets of simulated data with different waveforms as training data.

[0042] The learning unit 36 ​​uses at least multiple simulated data generated from abnormal time series data as training data, but normal simulated data generated from normal time series data may also be used as training data. In this case, the learning unit 36 ​​uses the normal simulated data and the labels of that normal simulated data (labels for normal) as training data. The learning unit 36 ​​may also use the abnormal time series data itself as training data. In this case, the learning unit 36 ​​uses the abnormal time series data and the labels of that abnormal time series data as training data. The learning unit 36 ​​may also use the normal time series data itself as training data. In this case, the learning unit 36 ​​uses the normal time series data and the labels of that normal time series data as training data.

[0043] The learning unit 36 ​​may set the order in which the training data dataset is input to the learning model based on the label type. For example, the learning unit 36 ​​may input the training model with a dataset of normal simulated data (simulated data with the label "normal") and train it, and then input the training model with a dataset of simulated data (simulated data with the label "abnormal"). For example, in this embodiment, it is preferable that the generation unit 34 inputs the training data dataset to the learning model in ascending order of the labels associated with the simulated data. That is, for example, first the learning unit 36 ​​trains the learning model with normal simulated data labeled 0 and its label 0. Next the learning unit 36 ​​trains the learning model with simulated data showing a downward trend associated with the label 1 and its label 1. Next the learning unit 36 ​​trains the learning model with simulated data showing an upward trend associated with the label 2 and its label 2. Finally the learning unit 36 ​​trains the learning model with simulated data generating a spike-like waveform associated with the label 3 and its label 3. The learning unit 36 ​​improves the output accuracy of the trained model by training the model based on the order of the labels. Note that the order in which the learning unit 36 ​​learns the simulated data and labels is not limited to ascending order. For example, the learning unit 36 ​​may learn the simulated data and labels in descending order, or it may learn them in any order, not limited to ascending or descending.

[0044] (Detection of abnormality) The anomaly detection device 10 uses the machine learning model developed as described above to determine (monitor) whether or not there is an anomaly in the rotating machinery using the time-series data targeted for anomaly detection. The method for determining whether or not there is an anomaly in the rotating machinery will be described below. In this embodiment, the anomaly detection device 10 performs both the process of training the learning model and the process of determining anomalies using the learning model described later. However, it is not limited to this, and the anomaly detection device 10 may perform only the process of determining anomalies using the learning model described later, without performing the process of training the learning model. In this case, the anomaly detection device 10 acquires the machine learning model, for example, by communication and stores it in the storage unit 18, and uses the learning model read from the storage unit 18 to determine whether or not there is an anomaly in the rotating machinery.

[0045] (Acquisition of target time series data) The data acquisition unit 30 acquires target time-series data, which is the time-series data to be used for anomaly detection. The data acquisition unit 30 acquires target time-series data from the sensors of the rotating machine, for example, via the communication unit 16.

[0046] (Judgment Department) The determination unit 38 inputs the target time series data into a trained model to perform anomaly detection on the target time series data. The determination unit 38 inputs the target time series data into a trained model that has learned the correspondence between time series data of the same type as the target time series data and the presence or absence of anomalies in the rotating machinery, and performs anomaly detection on the target time series data. That is, for example, if the target time series data is shaft vibration, the determination unit 38 inputs the target time series data into a trained model that has learned the correspondence between time series data of shaft vibration and the presence or absence of anomalies.

[0047] The determination unit 38 may input the target time series data (raw data) acquired by the data acquisition unit 30 directly into the trained model. Alternatively, the preprocessing unit 32 may perform preprocessing on the target time series data, and the preprocessed target time series data may be input into the trained model. The method for preprocessing the target time series data is the same as the preprocessing of abnormal time series data described above, so the explanation will be omitted.

[0048] When the trained model receives the target time series data as input, it outputs a determination result indicating whether or not there are anomalies in the target time series data. The determination unit 38 acquires this determination result output from the trained model. In this embodiment, the determination unit 38 acquires information indicating the label associated with the target time series data, calculated by the trained model, as the determination result. For example, the determination unit 38 may acquire the numerical value of the label associated with the target time series data from the trained model as the determination result, or it may acquire the probability that the target time series data is assigned to each label as the determination result.

[0049] Furthermore, when the determination unit 38 performs an abnormality determination on the waveform of the target time series data, it may make the abnormality determination after a predetermined time (for example, after a few minutes). For example, the determination unit 38 may determine that the waveform of the target time series data contains a spike-like waveform several minutes after it reaches a peak (for example, after detecting a downward trend in the waveform). Also, the determination unit 38 may have the learning unit 36 ​​retrain the determination result. For example, if the determination unit 38 incorrectly determines that the target time series data is abnormal, it will have the learning unit 36 ​​retrain the correct determination result. For example, if the determination unit 38 determines that the target time series data contains a spike-like waveform and is showing an upward trend, it will have the learning unit 36 ​​retrain that the target time series data contains a spike-like waveform.

[0050] (Output of results) The output control unit 40 outputs the determination result acquired by the determination unit 38. The output method by the output control unit 40 is arbitrary. For example, the output control unit 40 may output the determination result to the output unit 14 (for example, display it on a display). Alternatively, the output control unit 40 may transmit (output) the determination result to another device via the communication unit 16.

[0051] (Processing flow) The processing flow of the anomaly detection device 10 described above will now be explained. Figure 6 is a flowchart showing an example of the processing of the anomaly detection device. As shown in Figure 6, the anomaly detection device 10 acquires anomaly time series data using the data acquisition unit 30 (step S10). The anomaly detection device 10 performs preprocessing on the anomaly time series data using the preprocessing unit 32 (step S12). The anomaly detection device 10 generates multiple simulated data by correcting the anomaly time series data using the generation unit 34 (step S14). The anomaly detection device 10 prepares multiple datasets consisting of simulated data and labels indicating the state of the simulated data using the learning unit 36, and trains a learning model using multiple datasets as training data to generate a trained model (step S16). The anomaly detection device 10 acquires the target time series data to be subject to anomaly detection using the data acquisition unit 30, inputs the target time series data into the trained model using the determination unit 38, performs anomaly detection on the target time series data (step S18), and outputs the determination result using the output control unit 40 (step S20).

[0052] As described above, the anomaly detection device 10 according to this embodiment acquires anomaly time-series data, corrects the anomaly time-series data, and generates multiple simulated data. According to this embodiment, by generating multiple simulated data, it is possible to generate simulated data that more closely resembles the anomaly state. Therefore, according to this embodiment, anomalies can be detected with greater accuracy.

[0053] Here, in order to identify the cause of the anomaly, simulation data is generated by simulating the anomaly data, and this data is used to train a learning model, which then identifies the cause of the anomaly. This simulation data is generated based on the machine and is used to identify the cause of the anomaly at the fault location. However, the anomaly state data differs depending on the cause, etc., and generating multiple simulated data of possible scenarios makes it easier to determine the anomaly. In contrast, the anomaly detection device 10 of this embodiment makes it easier to determine the anomaly by performing the above processing, and can determine the anomaly with greater accuracy.

[0054] (effect) As described above, the abnormality determination device 10 according to the first aspect of this disclosure includes: a data acquisition unit 30 that acquires abnormal time series data, which is time series data of an abnormal state of a rotating machine; a generation unit 34 that corrects the abnormal time series data and generates a plurality of simulated data, which is time series data that simulates an abnormality; a learning unit 36 ​​that uses the simulated data and labels indicating the state of the simulated data as training data to train a learning model and generate a trained model; a determination unit 38 that inputs the target time series data of the rotating machine to the trained model and performs abnormality determination of the target time series data; and an output control unit 40 that outputs the result of the abnormality determination of the target time series data. According to this disclosure, abnormalities can be determined with greater accuracy.

[0055] An anomaly detection device 10 according to a second aspect of this disclosure is the anomaly detection device 10 according to a first aspect, wherein the generation unit 34 generates simulated data by changing at least one of the following: the period of the anomaly time series data, the timing at which the anomaly time series data peaks, the number of peaks in the anomaly time series data, and the intensity of the peaks in the anomaly time series data. According to this disclosure, anomalies can be detected with greater accuracy.

[0056] An anomaly detection device 10 according to a third aspect of this disclosure is an anomaly detection device 10 according to a second aspect, wherein the learning unit 36 ​​generates a trained model using the fact that the waveform of the simulated data is spike-shaped as a label, and the determination unit 38 detects that the waveform of the target time series data is spike-shaped as an anomaly detection result. According to this disclosure, anomalies can be detected with greater accuracy.

[0057] The anomaly detection device 10 according to the fourth aspect of this disclosure is an anomaly detection device 10 according to any of the first to third aspects, wherein the learning unit 36 ​​generates a trained model using as labels whether the simulated data is continuously increasing or continuously decreasing over time, and the determination unit 38 detects as an anomaly determination that the target time series data is continuously increasing or continuously decreasing over time. According to this disclosure, anomalies can be determined with greater accuracy.

[0058] An anomaly detection device 10 according to the fifth aspect of this disclosure is an anomaly detection device 10 according to any of the first to fourth aspects, further comprising a preprocessing unit 32 that normalizes anomaly time series data, and a learning unit 36 ​​generates a trained model by training a learning model with simulated data generated based on the normalized anomaly time series data. According to this disclosure, by normalizing the data, unnecessary data such as noise can be removed.

[0059] The anomaly detection device 10 according to the sixth aspect of this disclosure is the anomaly detection device 10 according to the fifth aspect, wherein the preprocessing unit 32 performs filtering processing to remove waveforms in which the amplitude of the peak intensity of the target time series data is less than a threshold. According to this disclosure, it is possible to detect the necessary data that has passed the filtering processing.

[0060] An anomaly detection device 10 according to the seventh aspect of this disclosure is an anomaly detection device 10 according to any of the first to sixth aspects, wherein the anomaly time-series data includes at least one of the shaft vibration data of the rotating machine and the bearing temperature data. According to this disclosure, anomalies can be determined with greater accuracy based on continuous data in a time series.

[0061] An abnormality detection device 10 according to the eighth aspect of this disclosure is an abnormality detection device 10 according to any of the first to seventh aspects, wherein the rotating machine includes at least one of a compressor and a turbine. According to this disclosure, abnormalities in the rotating machine can be determined with greater accuracy.

[0062] An anomaly detection method according to the ninth aspect of this disclosure includes the steps of: acquiring anomaly time series data, which is time series data of an abnormal state of a rotating machine; correcting the anomaly time series data to generate a number of simulated data, which are time series data that simulate the anomaly; training a learning model using the simulated data and labels indicating the state of the simulated data as training data to generate a trained model; inputting target time series data, which is time series data of the rotating machine, into the trained model to perform anomaly detection on the target time series data; and outputting the result of the anomaly detection on the target time series data. According to this disclosure, anomalies can be detected with greater accuracy.

[0063] A program according to the tenth aspect of this disclosure causes a computer to perform the following steps: acquire abnormal time series data, which is time series data of an abnormal state of a rotating machine; correct the abnormal time series data to generate multiple simulated data, which is time series data that simulates the abnormality; train a learning model using the simulated data and labels indicating the state of the simulated data as training data to generate a trained model; input target time series data, which is time series data of the rotating machine, into the trained model to perform abnormality determination of the target time series data; and output the result of the abnormality determination of the target time series data. According to this disclosure, abnormalities can be determined with greater accuracy.

[0064] Although embodiments of the present disclosure have been described above, the embodiments are not limited to those described herein. Furthermore, the aforementioned components include those that can be easily conceived by those skilled in the art, those that are substantially the same, and those that fall within the so-called equivalent range. Moreover, the aforementioned components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the gist of the embodiments described above. [Explanation of symbols]

[0065] 10 Abnormality determination device 30 Data Acquisition Unit 32 Pre-processing section 34 Generation part 36. Learning Department 38 Judgment section 40 Output control unit

Claims

1. A data acquisition unit that acquires abnormal time-series data, which is time-series data of abnormal conditions in rotating machinery, A generation unit that corrects the aforementioned abnormal time series data and generates multiple simulated data, which are time series data that simulate the abnormality. A learning unit generates a trained model by training a learning model using the aforementioned simulated data and labels indicating the state of the simulated data as training data. A determination unit inputs the target time series data, which is the time series data to be applied to the rotating machine, into the trained model and performs anomaly detection on the target time series data. An output control unit that outputs the result of an anomaly determination of the target time series data, including, Abnormality determination device.

2. The anomaly detection device according to claim 1, wherein the generation unit generates the simulated data by changing at least one of the following: the period of the anomaly time series data, the timing at which the anomaly time series data reaches a peak, the number of peaks in the anomaly time series data, and the intensity of the peaks in the anomaly time series data.

3. An anomaly detection device according to claim 1 or 2, wherein the learning unit generates the trained model using the fact that the waveform of the simulated data is spike-shaped as the label, and the determination unit detects that the waveform of the target time series data is spike-shaped as the result of the anomaly determination.

4. An anomaly detection device according to claim 1 or 2, wherein the learning unit generates the trained model using as a label whether the simulated data is continuously increasing or continuously decreasing over time, and the determination unit detects as an anomaly determination that the target time series data is continuously increasing or continuously decreasing over time.

5. The system further includes a preprocessing unit that performs normalization processing on the aforementioned abnormal time series data. The anomaly detection device according to claim 1 or 2, wherein the learning unit generates the trained model by training the learning model with the simulated data generated based on the standardized anomaly time series data.

6. The anomaly determination device according to claim 5, wherein the preprocessing unit performs filtering to remove waveforms in which the amplitude of fluctuations in the peak intensity of the target time series data is less than a threshold.

7. The abnormality determination device according to claim 1 or claim 2, wherein the abnormality time series data includes at least one of the shaft vibration data of the rotating machine and the bearing temperature data.

8. The abnormality determination device according to claim 1 or 2, wherein the rotating machine includes at least one of a compressor and a turbine.

9. The steps include acquiring abnormal time-series data, which is time-series data of abnormal conditions in rotating machinery, and The steps include correcting the aforementioned abnormal time series data to generate multiple simulated data sets, which are time series data that simulate the abnormality, The steps include: using the aforementioned simulated data and labels indicating the state of the simulated data as training data to train a learning model and generate a trained model; The steps include inputting the target time series data, which is the time series data to be applied to the rotating machine, into the trained model and performing anomaly detection on the target time series data, The steps include outputting the result of the anomaly detection of the target time series data, including, Abnormality determination method.

10. The steps include acquiring abnormal time-series data, which is time-series data of abnormal conditions in rotating machinery, and The steps include correcting the aforementioned abnormal time series data to generate multiple simulated data sets, which are time series data that simulate the abnormality, The steps include: using the aforementioned simulated data and labels indicating the state of the simulated data as training data to train a learning model and generate a trained model; The steps include inputting the target time series data, which is the time series data to be applied to the rotating machine, into the trained model and performing anomaly detection on the target time series data, The steps include outputting the result of the anomaly detection of the target time series data, Make the computer execute it. program.

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