Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Jinlu Sheng,Haowei Gao,Shaojiang Dong,Zhu Liu

doi:10.1177/1687814016661087

Abstract

To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

Highlights

Bearing is commonly used in rotary machinery; the bearing failing will cause the machine to break down
After the local mean decomposition (LMD) energy entropy is calculated, from one signal we can get one entropy value, and the bearing vibration signals are collected at different conditions and status, the resulting entropy will be in high dimensional, which will affect the classification effect of the support vector machine (SVM) model, and the kernel principal component analysis (KPCA) is used to reduce the feature dimension
From equations (11) and (12), the Morlet wavelet kernel SVM is constructed, and the new SVM will work as the model to separate the different bearing fault features

Summary

Introduction

Bearing is commonly used in rotary machinery; the bearing failing will cause the machine to break down. Based on the typical feature extraction by the LMD Shannon entropy and the weighted KPCA method, the support vector machine (SVM) is served as a classifier.[12] But the SVM model is not useful to process the nonlinear feature, some researcher proposed the combination method of SVM and wavelet theories to achieve the classification, and get better performance than other leaning machine models; in this research, the new SVM model is constructed based on the Morlet kernel.[13]. After the LMD energy entropy is calculated, from one signal we can get one entropy value, and the bearing vibration signals are collected at different conditions and status, the resulting entropy will be in high dimensional, which will affect the classification effect of the SVM model, and the KPCA is used to reduce the feature dimension. The centered kernel matrix can be expressed as follows[14,15]

K jK jTK

Methods

Findings

Conclusion