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Shock and Vibration
Volume 2016, Article ID 1835127, 12 pages
Research Article

Sparse Signal Representations of Bearing Fault Signals for Exhibiting Bearing Fault Features

1Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
2Institute of Reliability Engineering, School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu 610051, China
3Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
4School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China

Received 5 May 2015; Revised 7 October 2015; Accepted 12 October 2015

Academic Editor: Peng Chen

Copyright © 2016 Wei Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Sparse signal representations attract much attention in the community of signal processing because only a few coefficients are required to represent a signal and these coefficients make the signal understandable. For bearing faults’ diagnosis, bearing faults signals collected from transducers are often overwhelmed by strong low-frequency periodic signals and heavy noises. In this paper, a joint signal processing method is proposed to extract sparse envelope coefficients, which are the sparse signal representations of bearing fault signals. Firstly, to enhance bearing fault signals, particle swarm optimization is introduced to tune the parameters of wavelet transform and the optimal wavelet transform is used for retaining one of the resonant frequency bands. Thus, sparse wavelet coefficients are obtained. Secondly, to reduce the in-band noises existing in the sparse wavelet coefficients, an adaptive morphological analysis with an iterative local maximum detection method is developed to extract sparse envelope coefficients. Simulated and real bearing fault signals are investigated to illustrate how the sparse envelope coefficients are extracted. The results show that the sparse envelope coefficients can be used to represent bearing fault features and identify different localized bearing faults.