Exponent regularization and null space linearity discriminant analysis-based fault diagnosis method

Abstract

The invention discloses an exponent regularization and null space linearity discriminant analysis-based fault diagnosis method. Regularization discriminant analysis is combined with null space discriminant analysis, advantages of NSLDA and RLDA in terms of mode identification are integrated, a regularized intra-class sample matrix Sw1 is used for replacing a class sample matrix Sw in null space discriminant analysis, small sample problems can be further solved; in discrimination criteria for discriminant analysis, an exponential function is introduced, the regularized intra-class sample matrix Sw1 and an inter-class sample matrix Sb are respectively subjected to exponential operation, and therefore more characteristic information can be obtained; faults can be effectively and accurately identified, fault diagnosis precision can be effectively improved, and a new train of thought is put forward for small fault diagnosis based on data driving.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a fault diagnosis method based on exponential regularization null space linear discriminant analysis. Background technique [0002] With the complexity and scale of equipment in modern control systems, the abnormal detection and fault diagnosis of related systems have always been the focus of academic circles. Mechanical fault diagnosis is of great significance to ensure the safe operation of equipment. Once a mechanical equipment fails, if it cannot be found and dealt with in time, it will cause huge economic losses and casualties. Therefore, the safety and reliability of equipment and systems have become one of the focuses of people's attention. If the faults can be detected in a controllable industrial operation process in time to avoid the occurrence of abnormal events, it is necessary to perform reasonable fault diagnosis on complex systems. Problems to be solved. [0003] Fault...

AI Technical Summary

Problems solved by technology

[0009] Although nonlinear reduction can be achieved by introducing a kernel function, which can avoid the "small sample" problem to a certain extent, its unsupervised basic properties lead to blindness in the process of dimension reduction.

[0010] Null Space Linear Discriminant Analysis (NSLDA) uses the null space information of the intra-class sample matrix to extract discriminant features, which overcomes the small sample problem of LDA to a certain extent, but it is a linear feature extraction method after all. Extract nonlinear features, and its ability to solve singular matrices is limited

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