Multivariate Industrial Process Fault Detection Method Based on Master-Auxiliary PCA Model

Abstract

The present invention relates to a multi-variable industrial process fault detection method based on primary and secondary PCA models. Prioritizing the relative mutual information of fault and normal data, grouping variables with the help of generalized Dice, establishing a PCA model for the grouped data set as an auxiliary monitoring model, standardizing the test data set, and feeding the test data set to the main monitoring model And the auxiliary monitoring model projection, and calculate the statistics projected to the main monitoring model and auxiliary monitoring model, apply Bayesian theory to integrate the information of the variable group to obtain the total monitoring statistics, and judge the test data according to whether the monitoring statistics exceed the control limit set is faulty. The invention not only effectively reduces the omission and waste of some important prior fault information, but also improves the fault detection rate and fault detection performance by mining variable local information through variable grouping.

Description

technical field [0001] The invention belongs to the technical field of industrial process fault detection, and relates to a multivariable industrial process fault detection method based on a primary-assistant PCA model (English: Primary Assisted Principal Component Analysis, PA-PCA for short). Background technique [0002] Due to the increasing complexity of modern industrial systems, people pay more and more attention to process safety and product quality, and fault diagnosis plays an increasingly important role in industrial production. With the development of storage technology, a large amount of production process data is collected and recorded. Therefore, data-driven fault diagnosis methods have been widely used. Classical fault detection methods include Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Fisher Discriminant Analysis (FDA). Among them, the PCA method has become a hot spot in the field of control research in recent years, and h...

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Problems solved by technology

[0003] The present invention aims at the problem that the traditional PCA method cannot deeply dig out the local information related to the fault, resulting in low fault detection performance, etc., and provides a multivariable industrial process fault detection method based on the primary and secondary PCA model

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