Multi-variable industrial process fault detection method based on primary assisted PCA model

Abstract

The invention relates to a multi-variable industrial process fault detection method based on a primary assisted PCA model. The method comprises standardizing a normal data set and a prior fault data set; establishing a PCA model as a master monitoring model for the normal data set; calculating the relative mutual information of the prior fault and the normal data; grouping variables by virtue of generalized Dice; establishing a PCA model as an auxiliary monitoring model for a grouped data set; standardizing the test data set; projecting the test data set onto the master monitoring model and the auxiliary monitoring model separately; calculating the statistics of the test data set projected onto the master monitoring model and the auxiliary monitoring model; integrating the variable group information by using a Bayesian theory to obtain the total monitoring statistics; and determining whether the test data set has a fault according to whether the monitoring statistics exceed a control limit. The method not only reduces the omission and waste of some important prior fault information, but also mines the variable local information by variable grouping so as to improve a fault detection rate and improves fault detection performance.

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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