Real-time equipment abnormality detection device and method based on time sequence prediction model

Abstract

The invention discloses a real-time equipment abnormality detection device and method based on a time sequence prediction model. The device comprises a data layer, a logic control layer, a model center and a display layer. The data layer comprises a real-time database, a data buffer, a historical database and a time sequence data processing module. The logic control layer comprises a trainer and apredictor. The model center comprises a machine learning model. And the display layer comprises a result display module. Compared with a traditional equipment abnormality detection scheme, the methodhas the advantages that the time sequence prediction model is utilized, information of a certain time window can be utilized in the equipment operation process, the model precision of the system is higher, and the result better conforms to the reality. The incremental training mode is supported, so that the model can capture the operation conditions of different devices in time, and the machine learning model of the model center can be updated in time.

Description

technical field [0001] The invention relates to the technical field of equipment anomaly detection, in particular to a real-time equipment anomaly detection device and method based on a time series prediction model. Background technique [0002] Judging the abnormal operation of equipment based on data has been fully applied in manufacturing enterprises. In summary, the technical solutions for enterprise applications can be divided into the following two types: judgment based on experience threshold and abnormal judgment based on machine learning. The following two schemes are briefly introduced as follows: 1. Abnormal judgment based on empirical thresholds, such as figure 1 as shown, figure 1 This framework includes three parts: 1) Threshold setting: before real-time system debugging and operation, through the interactive module, the system operation engineer needs to input the operating experience threshold of the production system into the logic controller. 2) Implemen...

AI Technical Summary

Problems solved by technology

However, the abnormal judgment based on the experience threshold has the following deficiencies: 1), the operation is too dependent on the experience of the system operator

However, machine learning-based equipment operation abnormality diagnosis has the following deficiencies: 1) The abnormality diagnosis model based on traditional machine learning algorithms can only run time-sliced ​​data, that is, traditional machine learning algorithms cannot apply data before and after time-slicing, and then analyze the current The status of the operating equipment; 2) Similar to the framework for abnormal judgment based on experience thresholds, the working conditions of the system will change during operation, and the abnormal judgment framework based on traditional machine learning algorithms cannot update the mode of working condition changes in time , so there will be deviation or error in the result of equipment abnormal state judgment

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