A non-stationary industrial process monitoring method and system

By introducing temporal and spatial Laplace matrix constraints into the monitoring of non-stationary industrial processes, a spatiotemporally stationary subspace analysis method is proposed, which solves the problem of local spatiotemporal information loss in traditional methods and achieves higher monitoring accuracy and early fault detection.

CN121858929BActive Publication Date: 2026-06-23CENT SOUTH UNIV
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Patent Information

Application Number
CN202610322933.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-17
Publication Date
2026-06-23
Estimated Expiration
2046-03-17

AI Technical Summary

Technical Problem

Traditional non-stationary industrial process monitoring methods fail to effectively consider the local spatiotemporal correlations between process data, resulting in insufficient monitoring accuracy, difficulty in achieving accurate detection in the early stages of fault occurrence, and a tendency to generate false alarms.

Method used

The spatiotemporal stationary subspace analysis method is adopted. By constructing constraint terms for the time Laplacian matrix and the space Laplacian matrix, and combining generalized eigenvalue decomposition, the stationary projection matrix is ​​extracted and the monitoring index is calculated, thus preserving the spatiotemporal information of the data.

Benefits of technology

It significantly improves the accuracy and timeliness of non-stationary process monitoring, enabling early identification of minor faults, reducing false alarm rates, and increasing fault detection rates.

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Abstract

The application provides a non-stationary industrial process monitoring method and system, and the method comprises an offline training stage: a time Laplacian matrix and a space Laplacian matrix are calculated based on a historical data matrix; a target function of a stationary subspace analysis method is constructed, and a time constraint term of the time Laplacian matrix and a space constraint term of the space Laplacian matrix are added to the target function; the target function is solved to obtain a stationary projection matrix; stationary components and monitoring indexes of each sample in the data matrix X are calculated in turn based on the data matrix X; a control limit is determined by using a kernel density estimation method; and an online monitoring stage: based on real-time running data x, stationary components of the real-time running data x and corresponding real-time monitoring indexes are calculated according to the stationary projection matrix, and if the real-time monitoring indexes are greater than the control limit, it is judged that a non-stationary process operation has a fault; and the application can improve monitoring accuracy.
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