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

被引：15

作者：

Gu X. ^{[1
]}

Chen C. ^{[1
,2
]}

机构：

[1] School of Mechanical Engineering, Shenyang University of Technology, Shenyang

[2] Liaoning Engineering Center for Vibration and Noise Control, Shenyang

来源：

Chen, Changzheng (chencz6699@sina.com) | 2017年 / Hindawi Limited, 410 Park Avenue, 15th Floor, 287 pmb, New York, NY 10022, United States卷 / 2017期

基金：

中国国家自然科学基金;

关键词：

Failure analysis - Particle swarm optimization (PSO) - Stochastic systems - Signal denoising - Signal to noise ratio - Extraction - Roller bearings - Fault detection - Magnetic resonance - Circuit resonance;

D O I：

10.1155/2017/3595871

中图分类号：

学科分类号：

摘要：

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. © 2017 Xiaojiao Gu and Changzheng Chen.

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