Bearing fault diagnosis using a novel coding-statistic feature combined with NNC

作     者：Qiu, Mingquan Zhao, Zebo 

作者机构：Guizhou Minzu Univ Sch Mechatron Engn Guiyang Peoples R China 

出 版 物：《JOURNAL OF VIBROENGINEERING》 (J. Vibroeng.)

年 卷 期：2022年第24卷第5期

页      面：848-861页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0802[工学-机械工程] 

基　　金：Natural Science Foundation of Guizhou Province [ZKYB270] Research Foundation of Guizhou Minzu University [GZMUQN06] 

主　　题：bearing fault diagnosis waveform coding coding-statistic feature 

摘      要：The failures of rolling bearings usually cause the breakdown of rotating machinery. Therefore, bearing fault diagnosis is receiving more and more attentions. In this paper, a new coding-statistic feature is proposed for bearing fault diagnosis. Firstly, a waveform coding matrix (WCM) is drawn from each signal using a coding algorithm then a statistical feature is extracted from the WCM with a pre-defined dictionary. Secondly, all statistical features are processed using two-dimensional principal component analysis (2DPCA) to reduce redundant information and dimensionality. Finally, a nearest neighbor classifier (NNC) is employed to classify the bearing faults. Two bearing fault classification problems are utilized to demonstrate the effectiveness of the proposed scheme. Experimental results show that an excellent performance could be accomplished with the proposed scheme.