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International Journal of Rotating Machinery
Volume 2017, Article ID 3595871, 12 pages
https://doi.org/10.1155/2017/3595871
Research Article

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

1School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China
2Liaoning Engineering Center for Vibration and Noise Control, Shenyang 110870, China

Correspondence should be addressed to Changzheng Chen; moc.anis@9966zcnehc

Received 6 April 2017; Revised 10 August 2017; Accepted 27 August 2017; Published 1 November 2017

Academic Editor: Hyeong Joon Ahn

Copyright © 2017 Xiaojiao Gu and Changzheng Chen. 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.

Abstract

Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.