Industrial process fault detection method based on correction type independent element analysis and Bayes probability fusion

Abstract

The invention relates to an industrial process fault detection method based on correction type independent element analysis and Bayes probability fusion. A conventional fault detection method based on the correction type independent element analysis requires selection of a non-quadratic function to measure non-gaussianity. Different industrial process data or objects can cause a fact that enough experiential knowledge for guiding the selection of the non-quadratic function is hard to acquire in an actual application. By aiming at different non-quadratic functions, the normal data training of the industrial process is adopted to acquire different correction-type independent element models. The Bayes probability fusion method is used to integrate decision results of a plurality of fault detection models together to acquire a final probability type monitoring index. Compared with the prior art, the industrial process fault detection method is used to solve a model uncertainty problem caused by non-quadratic function diversity, and a plurality of model possibilities are fully considered, and the reliability of the fault detection models is enhanced to a great extent.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to an industrial process fault detection method based on modified independent element analysis and Bayesian probability fusion. Background technique [0002] The increasingly fierce market competition has put forward higher requirements for the production safety of modern industrial processes, and timely and reliable fault detection methods have become an indispensable part of the entire production system. Due to the complexity and large-scale trend of modern industrial processes, different types of failures will inevitably occur during operation. If the fault alarm cannot be triggered in time, it may cause an operation accident, seriously affect the quality of the product, and even cause immeasurable losses in life and property. Therefore, how to establish a more reliable and effective fault detection model to identify faults in the production process in a timely manner h...

AI Technical Summary

Problems solved by technology

However, the traditional ICA method has some unavoidable problems when building fault detection models

First, due to the random generation of initial values, the uncertainty of the established model

Second, there are three optional forms of the non-quadratic function used to estimate the degree of non-Gaussianity of the variables, which will also cause uncertainty in the built model

Although the modified independent element analysis method (MICA) can better overcome the first problem, the model uncertainty caused by the diversity of non-quadratic function choices will reduce the accuracy and reliability of the corresponding fault detection method. sex

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