Multi-working condition industrial monitoring method, device, equipment and medium based on dictionary learning

Abstract

This application relates to a multi-working-condition industrial monitoring method based on dictionary learning, including: obtaining unlabeled historical data containing pure values, measurement noise and abnormal values; constructing an unsupervised dictionary learning model based on the unlabeled historical data; The unsupervised dictionary learning model solves the dictionary, the sparse coding matrix and the outlier matrix; divides the dictionary into a plurality of sub-dictionaries according to the production condition information; obtains test data, and judges whether the test data is point of failure. The method can suppress the measurement noise and reduce the influence of the abnormal value, thereby effectively preventing false alarms on faults in industrial monitoring. Therefore, the accuracy of fault monitoring is improved, and the robustness of the monitoring method to outliers and noise in the data is ensured.

Description

technical field [0001] The present application relates to the field of industrial process monitoring, in particular to a dictionary learning-based multi-working-condition industrial monitoring method, device, equipment and medium. Background technique [0002] In modern industry, how to ensure production safety and improve product quality has important research value. It is against this background that process monitoring technology emerges. Most of the early process monitoring methods are based on mathematical models and knowledge-based monitoring. However, for the process industry, accurate mathematical mechanism models and complete expert knowledge are often difficult to obtain. Therefore, the process monitoring methods based on mathematical models and knowledge are usually difficult to be practically applied. [0003] With the widespread use of Distributed Control System (DCS) and various intelligent instruments in the process industry, a large amount of process data i...

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Problems solved by technology

[0005] Based on this, it is necessary to provide a multi-condition industrial monitoring method, device, equipment and medium based on dictionary learning to solve the problem of inaccurate monitoring of traditional industrial process monitoring methods

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