Synchro spline-kernelled chirplet extracting transform: A useful tool for characterizing time-varying features under noisy environments and applications to bearing fault diagnosis

Profiting from the good ability in time-frequency (TF) representation (TFR), synchroextracting transform (SET) has been extensively used for non-stationary signal processing. Vibration signals of bearing often contain strong noise, whose time-varying features are easily contaminated by noise information, affecting the TF readability of SET. Effectively identifying the time-varying features from noise are important for fault diagnosis. For this purpose, by introducing the frequency-rotating operator and frequency-shifting operator of spline-kernelled chirplet transform (SCT), this paper studies a new time-frequency analysis (TFA) method called synchro spline-kernelled chirplet extracting transform (SSCET). It is shown that the proposed method can effectively reveal the variations of time-varying features while retaining the energy concentration in noisy cases. Besides, the proposed method uses the studied binary TF image separation strategy to extract the time-varying features of multi-component signals. The comparative analysis results in simulations verified its effectiveness in energy concentration and anti-noise. The proposed method is finally successfully applied to the analysis of a bat echo signal as well as the fault diagnosis of a rolling bearing.