A review of stochastic resonance in rotating machine fault detection

• Published in: Mechanical systems and signal processing Vol. 116; pp. 230 - 260
• Main Authors: Lu, Siliang, He, Qingbo, Wang, Jun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2019
• Subjects: Digital signal processing; Nonlinear filtering; Rotating machine fault detection; Stochastic resonance; Weak signal detection; Rotating machine fault detection; Stochastic resonance; Weak signal detection; Digital signal processing; Nonlinear filtering
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2018.06.032

•Review stochastic resonance (SR) in rotating machine fault detection.•History, state-of-the-art methods, and applications of SR are reviewed.•Principle, application example, remarks are provided for different methods.•Case review and bibliography for fault detection of bearing, gearbox, etc.•Prospects of SR in rotating machine fault detection are discussed. 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.