Oscillatory time–frequency concentration for adaptive bearing fault diagnosis under nonstationary time-varying speed

Abstract

Being a promising post-processing solution, application of time–frequency concentration (TFC) suffers in an adaptive fault diagnosis scenario due to non-adaptive extraction of bearing fault signature from noise-associated vibration signals under nonstationary time-varying speed. Aiming at solving this problem, a novel method called oscillatory time–frequency concentration (OTFC) is proposed by oscillation based adaptive extraction of time-varying bearing fault signature with the help of tunable Q factor wavelet transform (TQWT). OTFC can effectively suppress unwanted noise while adaptively reveals the time-varying fault characteristic frequency (FCF) and corresponding harmonics with excellent readability. Two distinct numerical simulations and experimental case studies have been utilized to verify the performance of the proposed OTFC. The results show that the proposed OTFC not only performs better than the conventional TFC method namely synchrosqueezing wavelet transform (SST) but also shows superior performance than traditional continuous wavelet transform (CWT) and advanced TFC method namely multitaper synchrosqueezing wavelet transform (MST).