Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

作     者：GAO Huizhong LIANG Lin CHEN Xiaoguang XU Guanghua 

作者机构：School of Mechanical EngineeringXi'an Jiaotong University The 705 Research InstituteChina Shipbuilding Industry Corporation Key Laboratory for Modern Design and Rotor-Bearing System of Education MinistryXi'an Jiaotong University State Key Laboratory for Manufacturing Systems EngineeringXi'an Jiaotong University 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2015年第28卷第1期

页      面：96-105页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

基　　金：Supported by Shaanxi Provincial Overall Innovation Project of Science and Technology China(Grant No.2013KTCQ01-06) 

主　　题：time-frequency distribution non-negative matrix factorization rolling element bearing feature extraction 

摘      要：Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.