Wavelet-based Synchroextracting Transform: An effective TFA tool for machinery fault diagnosis. (September 2021)

• Record Type: Journal Article
• Authors: Shi, Zhenjin; Yang, Xu; Li, Yueyang; Yu, Gang
• Is Part Of: Control engineering practice. Volume 114(2021)
• Publication Date: 2021-09
• Subjects: Time-frequency analysis (TFA) -- Wavelet transform (WT) -- Wavelet-based synchrosqueezing transform (WSST) -- Wavelet-based Synchroextracting Transform (WSET); Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104884
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: In the literature of fault diagnosis for machinery, mechanical vibration signal analysis serves one of the most important approaches. By extracting features of vibration signal, such as instantaneous frequency (IF), instantaneous amplitude or spectral kurtosis, the fault of machinery can be effectively diagnosed. As an efficient tool of analyzing vibration signal, time–frequency (TF) analysis (TFA) technology has been widely employed in this area. Restricted by Heisenberg uncertainty theory, the TF resolution of the traditional linear TF analysis technique may not be optimal. To overcome this problem, in this paper, inspired by wavelet-based synchrosqueezing transform (WSST), synchroextracting transform (SET) and the ideal TFA principle, we present a novel wavelet-based TFA approach, which is named wavelet-based synchroextracting transform (WSET) and acts as a TF post-processing technology. The core idea of WSET is that we only extract wavelet transform TF spectrum of signal in scale correspond to IF, and get rid of burry TF energy. This proposed method is capable of enhancing the concentration of TF representation. In the current study, firstly, the theory of the WSET is analytically deduced. Secondly, the validity of WSET is proved through processing the nonstationary and multi-component bat signal. Finally, we employ two benchmarks of rotor and rolling bearing to verify the effectiveness of WSET to extract the failure features for malfunction diagnosis.