Fault diagnosis method based on binary classification Fisher discriminant analysis

Abstract

The invention discloses a fault diagnosis method based on the binary classification Fisher discriminant analysis and aims to improve the applicability and the classification accuracy through variable selection when Fisher discriminant analysis models are used for fault diagnosis. The method comprises the steps that a set of characteristic variables through which the fault types are most distinguished from normal data is selected by means of the genetic algorithm, then a binary classification Fisher discriminant analysis model between the normal data and each type of fault data is built by means of the characteristic variables, and finally, fault classification diagnosis is performed by means of a plurality of binary classification Fisher discriminant analysis models. Because the genetic algorithm is adopted to optimize and select the set of characteristic variables, the disturbing influence of non-characteristic variables can be maximally reduced, and a dimensionality reduction effect can be further achieved, so that the limitation of the limited quantities of reference fault samples to modeling is reduced to a certain extent. Besides, the binary classification discriminant analysis models are adopted, so that each model is targeted on a specific fault type, and accordingly the model classification accuracy can be improved.

Description

A Fault Diagnosis Method Based on Two-Class Fisher Discriminant Analysis technical field The invention relates to an industrial fault diagnosis method, in particular to a fault diagnosis method based on two-class Fisher discriminant analysis. Background technique The increasingly complex and large-scale industrial process objects put forward higher and higher requirements for the performance of fault detection and diagnosis systems, not only to trigger fault alarms in a timely manner, but also to accurately identify the current fault type. Considering the complex characteristics of process objects, it is almost impossible to establish the corresponding mechanism model. In this regard, theoretical researchers and practitioners propose to use the data collected in the production process to implement fault detection and diagnosis. Under the background of industrial "big data", data-driven process monitoring methods and technologies have been unprecedentedly developed and app...

AI Technical Summary

Problems solved by technology

However, when there are many types of faults, the accuracy of the Fisher discriminant model based on variable selection still cannot meet the requirements

However, variable selection is still an effective way to improve the classification ability of the model.

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