Non-linear fault diagnosis method based on core pivot element analysis

Abstract

A non-linear fault diagnosis method based on kernel pivot analysis carries out non-linear analysis with the monitor data of a normal state system to pick up the non-linear pivot information and applies the kernel pivot model of important non-linear structure system at normal state, images the new measured data of the system to the kernel pivot model to reconstruct the data to the character information picked up by the new test data, judges the working condition of the system by the difference between the computed new tested data and reconstruction to it by the kernel pivot model. When the difference exceeds the top confidence limit, the new test data is judged to be fault, and the system is at fault state.

Description

technical field [0001] The invention relates to a nonlinear fault diagnosis method, in particular to a nonlinear fault diagnosis method based on nuclear principal component analysis. It is used in the technical field of electronic information engineering. Background technique [0002] Due to the requirements of product quality, economic benefits, safety and environmental protection, industrial processes and related control systems have become very complex. In order to ensure the normal operation of industrial systems, fault diagnosis and detection play a very important role in industrial processes. In recent years, the application of multivariate statistical analysis to process monitoring and fault diagnosis has been extensively studied. Principal component analysis is one of the widely used methods in industrial process fault diagnosis and detection. [0003] After searching the documents of the prior art, it was found that "Computer-based monitoring and fault diagnosis: ...

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Problems solved by technology

Therefore, it is inappropriate to apply PCA to the fault diagnosis of nonlinear industrial systems

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