Bearing variable-condition fault diagnosis method based on LMD-SVD (Local Mean Decomposition-Singular Value Decomposition) and extreme learning machine

Abstract

The invention discloses a fault diagnosis method based on local mean decomposition (LMD), singular value decomposition (SVD) and an extreme learning machine (ELM), and aims to increase the accuracy of bearing fault diagnosis under a variable condition. The method comprises the following steps: firstly, decomposing a nonlinear and unstable original vibration signal into a series of product functions (PFs) by adopting an efficient adaptive signal processing method LMD, wherein each PF component is a product of an envelope signal and a pure frequency modulated signal having physical meaning; secondly, processing the PF components by adopting the SVD to compress a feature vector scale and obtain a more stable feature vector value; lastly, classifying a bearing fault state by applying the ELM having higher application computation efficiency and higher classification accuracy based on an extracted feature vector. By adopting the fault diagnosis method based on LMD-SVD-ELM, a set of complete and valid variable-condition fault diagnosis scheme is provided for a bearing. The method has a very good practical engineering application value.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis under variable working conditions, in particular to a method based on local mean decomposition (local mean decomposition, LMD), singular value decomposition (singular value decomposition, SVD) and extreme learning machine (extreme learning machine, ELM) fault diagnosis method. Background technique [0002] With the continuous development of modern large-scale production and the continuous advancement of science and technology, the scale of electromechanical systems is becoming larger and larger, and the structure is becoming more and more complex. People's requirements for reliable and safe operation of equipment are getting higher and higher. Bearings are important and common components in rotating machinery, and their performance has a crucial impact on the reliable operation of the entire system. Bearing failure may lead to sudden shutdown of rotating machinery, which in turn l...

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Problems solved by technology

However, the ANN method needs to manually set more parameters in the application, the training speed is slow, and it is easy to fall into the local extremum

However, SVM also faces the problem of difficult selection of multiple parameters. Some existing parameter optimization methods often increase the training time of SVM.

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