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Bearing Fault Diagnosis Using Singular Value Decomposition and Hidden Markov Modeling

[+] Author Affiliations
Dong Wang, Qiang Miao, Rui Sun, Hong-Zhong Huang

University of Electronic Science and Technology of China, Chengdu, Sichuan, China

Paper No. DETC2009-86471, pp. 811-817; 7 pages
doi:10.1115/DETC2009-86471
From:
  • ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: ASME/IEEE 2009 International Conference on Mechatronic and Embedded Systems and Applications; 20th Reliability, Stress Analysis, and Failure Prevention Conference
  • San Diego, California, USA, August 30–September 2, 2009
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4900-2 | eISBN: 978-0-7918-3856-3
  • Copyright © 2009 by ASME

abstract

Condition monitoring and fault diagnosis of bearings are of practical significance in industry. In order to get a feature containing different fault signatures, this paper uses Wavelet Transform (WT), Wavelet Lifting Scheme (WLS) and Empirical Mode Decomposition (EMD), respectively, to decompose signal into different frequency bands. Then, Singular Value Decomposition (SVD) is utilized to extract intrinsic characteristic of signal from obtained matrix. These singular value vectors are regarded as inputs to Hidden Markov Models (HMM) for identification of machinery health condition. In this research, the fault diagnosis system is validated by motor bearing data, including normal bearings, inner race fault bearings, outer race fault bearings and roller fault bearings. Analysis results show that this method is effective in bearing fault diagnosis and its classification rate is excellent.

Copyright © 2009 by ASME

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