Realizing the empirical mode decomposition by the adaptive stochastic resonance in a new periodical model and its application in bearing fault diagnosis

We investigate a multi-frequency signal that cannot be decomposed by empirical mode decomposition directly. Moreover, this kind of signal in the noisy background cannot be decomposed successfully by the traditional stochastic resonance with bistable system yet. We propose a new method which using th...

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Bibliographic Details
Published in:Journal of mechanical science and technology 2017, 31(10), , pp.4599-4610
Main Authors: Zhang, Jingling, Huang, Dawen, Yang, Jianhua, Liu, Houguang, Liu, Xiaole
Format: Article
Language:English
Subjects:
Background noise
Computer simulation
Control
Decomposition
Dynamical Systems
Engineering
Fault diagnosis
Industrial and Production Engineering
Mechanical Engineering
Particle swarm optimization
Stochastic resonance
Vibration
기계공학
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Summary:We investigate a multi-frequency signal that cannot be decomposed by empirical mode decomposition directly. Moreover, this kind of signal in the noisy background cannot be decomposed successfully by the traditional stochastic resonance with bistable system yet. We propose a new method which using the empirical mode decomposition combined the adaptive stochastic resonance in a new periodical model to solve this problem. The results show that the proposed method decomposes the multi-frequency signal perfectly. Meanwhile, the general scale transformation and random particle swarm optimization algorithm are used to help obtain a better result in the process of optimization. Through using this new method, the simulation results are satisfactory. More importantly, this new method also shows good performance in the application of bearing fault diagnosis.
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-017-0906-6