Intelligent fault diagnosis of rolling element bearings based on bispectrum principal components analysis

被引：0

作者：

Zhang, Rui-Ge ^{[1
]}

Tan, Yong-Hong ^{[2
]}

机构：

[1] College of Mechanical and Electrical Engineering, Sanming University, Sanming,365004, China

[2] College of Information, Mechanical and Electronic Engineering, Shanghai Normal University, Shanghai,200234, China

来源：

Zhendong Gongcheng Xuebao/Journal of Vibration Engineering | 2014年 / 27卷 / 05期

关键词：

Fault detection - Principal component analysis - Eigenvalues and eigenfunctions - Failure analysis - Roller bearings;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper presents a method for the fault diagnosis of rolling element bearings using the principal components analysis of the bispectrum and hidden Markov models (HMM). It has been shown that the bispectrum magnitudes and distribution characteristics are significantly different under different failure status of bearings, but much more similar for the same type of faults under different operation conditions. Thus, the bispectrum can be used as the observation quantity for HMM to detect the fault category. Then, a principal component analysis (PCA) based method is used to extract the significant components so as to reduce the dimension of the feature vector, and the number of the principal components is also determined with the eigenvalue cumulative contribution rate. Finally, we conducted a number of experiments under different operation conditions and fault severities to validate the efficiency of the proposed method. The results show that the proposed method is effective in the detection of the various bearings faults, and also robust to the variation of the operation conditions. ©, 2014, Nanjing University of Aeronautics an Astronautics. All right reserved.

引用

页码：763 / 769