Automatic bearing fault diagnosis using particle swarm clustering and Hidden Markov Model

被引：67

作者：

Yuwono, Mitchell ^{[1
]}

Qin, Yong ^{[3
]}

Zhou, Jing ^{[1
]}

Guo, Ying ^{[2
]}

Celler, Branko G. ^{[2
]}

Su, Steven W. ^{[1
]}

机构：

[1] Univ Technol Sydney, Fac Engn & Informat Technol, 15 Broadway, Ultimo, NSW 2007, Australia

[2] CSIRO, Div Computat Informat, Marsfield, NSW 2122, Australia

[3] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2016年 / 47卷

关键词：

Fault detection and diagnosis; Rolling bearing defect diagnosis; Data clustering; Hidden Markov Model; Wavelet kurtogram; Cepstral analysis;

D O I：

10.1016/j.engappai.2015.03.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Ball bearings are integral elements in most rotating manufacturing machineries. While detecting defective bearing is relatively straightforward, discovering the source of defect requires advanced signal processing techniques. This paper proposes an automatic bearing defect diagnosis method based on Swarm Rapid Centroid Estimation (SRCE) and Hidden Markov Model (HMM). Using the defect frequency signatures extracted with Wavelet Kurtogram and Cepstral Littering, SRCE+HMM achieved on average the sensitivity, specificity, and error rate of 98.02%, 96.03%, and 2.65%, respectively, on the bearing fault vibration data provided by Case School of Engineering of the Case Western Reserve University (CSE) which warrants further investigation. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：88 / 100

页数：13

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