Industrial system production anomaly detection and diagnosis method based on multi-dimensional sensing data

Abstract

The invention discloses an industrial system production anomaly detection and diagnosis method based on multi-dimensional sensing data, and the method comprises the steps: carrying out the preprocessing of a multi-dimensional sensing data sample, and dividing the preprocessed multi-dimensional sensing data sample into a plurality of sub-samples through a sliding window; an automatic coding machine is adopted, and an anomaly detection model is obtained based on normal subsample training in an unsupervised training mode; training a classification model according to the anomaly detection model; and carrying out real-time detection and diagnosis on the production abnormity of the industrial system based on the abnormity detection model and the classification model. The problem that abnormality diagnosis is difficult to carry out under the condition that a black box model is used for multi-dimensional sensing data abnormality detection at present is solved.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a method for detecting and diagnosing abnormal production of an industrial system based on multi-dimensional sensing data. Background technique [0002] The Industrial Internet aims to achieve sharper and more efficient automation control and resource allocation of industrial manufacturing systems, while improving the production efficiency of smart factories. However, because the Industrial Internet breaks the boundaries between the cyber world and the physical world, it makes industrial manufacturing systems more vulnerable to external malicious acts. In addition, production problems such as equipment failure, performance degradation, and quality defects are unavoidable in industrial manufacturing systems. If abnormal conditions such as intrusion and failure in industrial production cannot be detected in time, it may bring serious losses to the entire manufactur...

AI Technical Summary

Problems solved by technology

However, due to the complexity of industrial production data and the high cost of manual labeling, the acquired industrial production data is basically unlabeled, so the anomaly detection model needs to be trained in an unsupervised manner

Especially for new deep unsupervised learning models such as autoencoders, it is almost impossible for existing deep learning interpretable frameworks to learn the association between abnormal samples and semantic features, resulting in the inability to explain deep unsupervised learning models.

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