The DOI matches its Crossref record; 135 of the 135 references it cites were found in a registry or an index.
A review of stochastic resonance in rotating machine fault detection
Abstract
Abstract Condition-based monitoring and machine fault detection play important roles in industry as they can ensure safety and reduce breakdown loss. Weak signal detection is an essential stage in many signal processing-based machine fault detection methods because the acquired machine signals are always corrupted by heavy background noise. Stochastic resonance (SR) is a nonlinear phenomenon in which the weak signal can be enhanced with the assistance of proper noise. Due to this distinct merit, SR has been extensively investigated in rotating machine fault detection. Given this, the present study is committed to providing a comprehensive review of SR from history to state-of-the-art methods and finally to research prospects, along with the applications in rotating machine fault detection. First, the classical SR theory including the history, merits and limitations is introduced and discussed, and the basic research progress of SR is reviewed. Second, the modified SR methods designed for processing the rotating machine signals are reviewed and summarized. Third, applications of SR for analyzing different kinds of rotating machine fault signals are introduced. Finally, the open problems, challenges and research prospects of SR in rotating machine fault detection are discussed.
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Cited by
Wang, X., Lu, S., Huang, W., Wang, Q., Zhang, S., & Xia, M. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3051668 · Cited by 65
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Metadata
- Authors
- Lu, S., He, Q., & Wang, J.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 116
- Pages
- 230-260
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W2883254659
- Cited by
- 382
- References
- 135
- FWCI
- 26.84
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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