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Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD
Joint Authors
Source
International Journal of Rotating Machinery
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gu, Xiaojiao& Chen, Chang-Zheng. 2017. Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD. International Journal of Rotating Machinery،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1169513
Modern Language Association (MLA)
Gu, Xiaojiao& Chen, Chang-Zheng. Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD. International Journal of Rotating Machinery No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1169513
American Medical Association (AMA)
Gu, Xiaojiao& Chen, Chang-Zheng. Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD. International Journal of Rotating Machinery. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1169513
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169513