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JP-7855034-B2 - Anomaly Severity Calculation System and Method

JP7855034B2JP 7855034 B2JP7855034 B2JP 7855034B2JP-7855034-B2

Inventors

  • 土肥 宏太
  • 遠藤 隆
  • 川口 洋平

Assignees

  • 株式会社日立製作所

Dates

Publication Date
20260507
Application Date
20240829

Claims (10)

  1. An abnormality calculation system for calculating the degree of abnormality of the target equipment, A conceptual classification unit that assigns a predetermined conceptual classification based on the identification number of the device, A feature vector extraction unit extracts feature vectors based on sensor data obtained from a sensor corresponding to the aforementioned device, A likelihood calculation unit that calculates the likelihood of the feature vector using a machine learning model obtained from a training database, A loss calculation unit that calculates the loss using a loss function defined as a function of the likelihood calculated by the likelihood calculation unit, A model update unit updates the machine learning model using the loss calculated by the loss calculation unit and the learned machine learning model. The system includes an anomaly calculation unit that calculates an anomaly score based on the likelihood calculated by the likelihood calculation unit, The concept classification unit assigns different concept classifications to the device when assigning a concept classification based on the device's identification number, depending on whether the normal distribution of the device's sensor data is the same as or different from the normal distribution of the sensor data of the target device for which the degree of abnormality is calculated. The loss calculation unit calculates the loss value using a loss function that increases the loss value as the value of the negative log-likelihood based on the likelihood derived from the sensor data of the device assigned the same conceptual type as the target device is larger, and increases the loss value as the value of the negative log-likelihood based on the likelihood derived from the sensor data of the device assigned a different conceptual type from the target device is smaller. The model update unit updates the machine learning model in such a way that it reduces the loss value calculated by the loss calculation unit. Anomaly severity calculation system.
  2. A single model may include multiple variations. The model of the aforementioned target device and the model of other devices that are of the same concept belonging to a common higher-level concept as the aforementioned target device, which are devices to which a different conceptual type from the conceptual type of the aforementioned target device has been assigned, are common. The model number of the aforementioned target device and the model numbers of other devices of the same concept are different from each other. The abnormality level calculation system according to claim 1.
  3. The aforementioned identification number is information assigned according to the model. The abnormality level calculation system according to claim 1.
  4. The aforementioned conceptual type is set to "1" for the target device, and to "0" for other devices of the same concept belonging to a common higher-level concept with the target device, which are devices to which a different conceptual type from the target device has been assigned . The abnormality level calculation system according to claim 1.
  5. Multiple devices that exhibit measurable physical changes in response to operation, which belong to a common model and are pre-grouped together, One of the target devices is selected from the grouped devices. Of the grouped devices, at least one device other than the target device is selected as another device of an equivalent concept belonging to a common higher-level concept with the target device, which is a device to which a different conceptual type from the conceptual type of the target device has been assigned. The abnormality level calculation system according to claim 1.
  6. The anomaly calculation system comprises a trained model likelihood calculation unit and a training model likelihood calculation unit, which is the likelihood calculation unit. The aforementioned learning model likelihood calculation unit calculates the likelihood of the feature vector using the machine learning model obtained from the learning database, not only during learning but also during operation. The aforementioned trained model likelihood calculation unit calculates the likelihood of the feature vector using the trained machine learning model obtained from the trained database during operation. The abnormality level calculation system according to claim 1.
  7. The learning model likelihood calculation unit calculates the likelihood by reading the machine learning model from the learning database which stores the machine learning model that is updated sequentially during operation. The trained model likelihood calculation unit calculates the likelihood by reading the trained machine learning model from the trained database which stores the trained machine learning model at the start of operation. The abnormality level calculation system according to claim 6.
  8. The abnormality calculation system further comprises an equivalence concept type assignment unit that sets weights for the other devices according to the similarity of the lower concepts of the target device and the lower concepts of other devices that are equivalence concepts belonging to a common higher concept with the target device, which are devices to which a different conceptual type from the conceptual type of the target device has been assigned. The loss calculation unit calculates the loss value using a loss function that includes a product obtained by multiplying the negative log-likelihood value, which is based on the likelihood derived from the sensor data of the other device, by the weight set for the other device. The abnormality level calculation system according to claim 1.
  9. The aforementioned device is a device that generates sound or vibration in response to its operation. The abnormality level calculation system according to claim 1.
  10. An abnormality calculation method in which a computer calculates the degree of abnormality of the target equipment, The aforementioned computer, A predetermined conceptual category is assigned based on the device's identification number. Based on the sensor data obtained from the sensor corresponding to the aforementioned device, a feature vector is extracted. Using a machine learning model obtained from a training database, the likelihood of the feature vector is calculated. The loss is calculated using the loss function defined as the calculated likelihood function, The machine learning model is updated using the calculated loss and the trained machine learning model. A method for calculating the degree of abnormality based on the calculated likelihood when detecting an abnormality in the aforementioned target equipment, When the computer assigns a conceptual type based on the identification number of the device, it assigns different conceptual types depending on whether the normal distribution of the sensor data of the device is the same as or different from the normal distribution of the sensor data of the target device for which the degree of abnormality is calculated. When the computer calculates the loss using the loss function, it uses a loss function that increases the value of the loss as the value of the negative log-likelihood based on the likelihood derived from the sensor data of the device assigned the same conceptual type as the target device is larger, and increases the value of the loss as the value of the negative log-likelihood based on the likelihood derived from the sensor data of the device assigned a different conceptual type from the target device is smaller. When the computer updates the machine learning model, it updates the machine learning model in such a way that it reduces the calculated loss value. Abnormality degree calculation method.

Description

This invention relates to a system and method for calculating the degree of anomaly. Generally, in anomaly detection, it is difficult to obtain a sufficient amount of anomaly data for all types of anomalies. Therefore, a common approach is to estimate the normal distribution using only normal data and then use that to determine anomalies. On the other hand, even in methods that estimate the normal distribution using only normal data, there are cases where sufficient normal data cannot be collected, or where the normal distribution changes while a sufficient amount of normal data is being collected. Therefore, there is a need for a method that can obtain sufficient detection accuracy with a small amount of normal data. Therefore, a technique has been proposed (Patent Document 1) that improves the accuracy of estimating the normal distribution by using normal data from machines similar to the target machine, in addition to normal data from the target machine, thereby achieving sufficient detection accuracy even when the normal data for the target machine is small. Patent Document 1 states: "The processing unit 11 calculates a first value, which indicates the extent to which the derived state value of the monitored device deviates from a first distribution region representing the distribution of state values for each device of the same type as the monitored device. The processing unit 11 also calculates a second value, which indicates the extent to which the derived state value of the monitored device deviates from a second distribution region representing the distribution of past state values for each device of the monitored device. Based on the first and second values, the processing unit 11 determines whether or not the monitored device is abnormal." Japanese Patent Publication No. 2019-008354 An explanatory diagram showing the overall overview of this embodiment.A diagram illustrating the process of extracting concept types from input.A diagram illustrating the process of calculating loss using data from a training database.An explanatory diagram showing the relationship between the negative log-likelihood and the number of model updates.A diagram illustrating the method for determining whether relearning is necessary.Hardware and software configuration diagram of the abnormality severity calculation device.Block diagram of the feature vector extraction unit.Block diagram for learning.Processing flow of the anomaly score calculation system during training.Block diagram used when calculating the degree of abnormality.Processing flow of the anomaly calculation system when calculating the anomaly severity.An explanatory diagram showing the differences between the present invention and conventional methods.A block diagram relating to the second embodiment, used for calculating the degree of abnormality.Processing flow of the anomaly calculation system when calculating the anomaly severity.A diagram illustrating the block configuration during learning, relating to the third embodiment.Processing flow of the anomaly score calculation system during training.Block diagram used when calculating the degree of abnormality.Processing flow of the anomaly calculation system when calculating the anomaly severity. The embodiments of the present invention will be described below with reference to the drawings. The abnormality calculation system according to this embodiment improves the accuracy of detecting abnormalities by using the sensor data of both the detection target device and the sensor data of the equivalent device, even when these devices follow different normal distributions in an environment where the detection target device and other devices (also called equivalent devices or other devices) are mixed. As a result, the abnormality calculation system of this embodiment can achieve sufficient detection accuracy using a relatively small amount of normal data from the detection target device. The abnormality calculation system of this embodiment obtains input D0 from n devices M, including the target device, that are identical in terms of a higher-level concept, for example, in terms of model. Input D0 includes a model number D1, which is an example of a lower-level concept of the model, and sensor data D2. The abnormality level calculation system of this embodiment includes a concept classification unit 11 that outputs a concept classification D3 indicating whether each device is a device to be detected (target device) or a device with the same concept as the target device (other device), based on the model number D1. Furthermore, the anomaly calculation system of this embodiment includes a feature vector extraction unit 12 that outputs feature quantities D4 from sensor data D2, a likelihood calculation unit 13 that outputs likelihood D5 from feature quantities D4 using a machine learning model, a loss calculation unit 14 that calculates a loss value D6 using a loss function defined as a function of likelihood D5