Fault Diagnosis of Rolling Bearings Based on Path Graph Laplacian Norm and Mahalanobis Distance

被引：1

作者：

Yang H. ^{[1
,2
]}

Yu D. ^{[1
]}

Gao Y. ^{[1
]}

机构：

[1] State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha

[2] School of Mechanical and Optoelectronic Physics, Huaihua University, Huaihua, 418008, Hunan

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2017年 / 28卷 / 20期

关键词：

Fault diagnosis; Laplacian operator; Rolling bearing; Signal processing;

D O I：

10.3969/j.issn.1004-132X.2017.20.015

中图分类号：

学科分类号：

摘要：

In order to extract fault features of vibration signals of rolling bearings effectively, the graph signal processing technology was introduced into fault diagnosis of rolling bearings. Vibration signals of a rolling bearing was firstly transformed into path graph signal. Then, the path graph Laplacian norm was calculated as characteristic parameters, and the standard feature space was obtained. Finally, the Mahalanobis distance of test samples and the standard feature space were used to identify fault patterns of the rolling bearings. Analytic results of the practical vibration signals of rolling bearings demonstrate that the proposed method may be used to diagnose the rolling bearing faults effectively. © 2017, China Mechanical Engineering Magazine Office. All right reserved.

引用

页码：2493 / 2499and2519

共 27 条

[1]

Prabhakar S., Mohanty A.R., Sekhar A.S., Application of Discrete Wavelet Transform for Detection of Ball Bearing Race Faults, Tribology International, 35, 12, pp. 793-800, (2002)

[2]

Sreejith B., Verma A.K., Srividya A., Fault Diagnosis of Rolling Element Bearing Using Time-domain Features and Neural Networks, IEEE Region 10 and the Third International Conference on Industrial and Information Systems, pp. 1-6, (2009)

[3]

Yang H., Mathew J., Ma L., Intelligent Diagnosis of Rotating Machinery Faults-a Review, The 3rd Asia-Pacific Conference on Systems Integrity and Maintenance, pp. 385-392, (2002)

[4]

Jardine A.K.S., Lin D., Banjevic D., A Review on Machinery Diagnostics and Prognostics Implementing Condition-based Maintenance, Mechanical Systems & Signal Processing, 20, 7, pp. 1483-1510, (2006)

[5]

Tse P.W., Peng Y.H., Yam R., Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis-Their Effectiveness and Flexibilities, Journal of Vibration & Acoustics, 123, 3, pp. 303-310, (2001)

[6]

Altmann J., Mathew J., Multiple Band-pass Autoregressive Demodulation for Rolling-element Bearing Fault Diagnosis, Mechanical Systems & Signal Processing, 15, 5, pp. 963-977, (2001)

[7]

Klein R., Ingman D., Braun S., Non-stationary Signals: Phase-energy Approach Theory and Simulations, Mechanical Systems & Signal Processing, 15, 6, pp. 1061-1089, (2001)

[8]

Dong H., Qi K., Chen X., Et al., Sifting Process of EMD and Its Application in Rolling Element Bearing Fault Diagnosis, Journal of Mechanical Science & Technology, 23, 8, pp. 2000-2007, (2009)

[9]

Zhou Y., Chen J., Dong G.M., Et al., Wigner-Ville Distribution Based on Cyclic Spectral Density and the Application in Rolling Element Bearings Diagnosis, Journal of Mechanical Engineering Science, 225, 12, pp. 2831-2847, (2011)

[10]

Chebil J., Noel G., Mesbah M., Et al., Wavelet Decomposition for the Detection and Diagnosis of Faults in Rolling Element Bearings, Jordan Journal of Mechanical & Industrial Engineering, 3, 4, pp. 260-267, (2009)

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