Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 84, Issue -, Pages 731-746Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2016.08.030
Keywords
Mechanical fault diagnosis; Signal processing; Stochastic resonance; Output saturation
Categories
Funding
- National Natural Science Foundation of China [51475355, 51421004]
- Young Talent Support Plan of Central Organization Department
- Fundamental Research Funds for Central Universities [2012jdgz01, CXTD2014001]
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In mechanical fault diagnosis, most traditional methods for signal processing attempt to suppress or cancel noise imbedded in vibration signals for extracting weak fault characteristics, whereas stochastic resonance (SR), as a potential tool for signal processing, is able to utilize the noise to enhance fault characteristics. The classical bistable SR (CBSR), as one of the most widely used SR methods, however, has the disadvantage of inherent output saturation. The output saturation not only reduces the output signal-to-noise ratio (SNR) but also limits the enhancement capability for fault characteristics. To overcome this shortcoming, a novel method is proposed to extract the fault characteristics, where a piecewise bistable potential model is established. Simulated signals are used to illustrate the effectiveness of the proposed method, and the results show that the method is able to extract weak fault characteristics and has good enhancement performance and anti-noise capability. Finally, the method is applied to fault diagnosis of bearings and planetary gearboxes, respectively. The diagnosis results demonstrate that the proposed method can obtain larger output SNR, higher spectrum peaks at fault characteristic frequencies and therefore larger recognizable degree than the CBSR method. (C) 2016 Elsevier Ltd. All rights reserved.
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