Synchrosqueezing extracting transform and its application in bearing fault diagnosis under non-stationary conditions

Abstract

The measured vibration signals of mechanical equipment with defects are generally non-stationary. Time–frequency analysis is an effective tool in processing non-stationary signals. However, the concentration of time–frequency representation will greatly affect the performance of feature extraction. Thus, a more concentrated time–frequency analysis method plays an important role in mechanical signal processing and fault diagnosis. A novel time–frequency analysis method, called synchrosqueezing extracting transform has been proposed in this study. The proposed method can not only well extract the time-varying information of the nonstationary signal then achieve a better-concentrated time–frequency representation, but also have better noise robustness and lower time-consuming than the traditional time–frequency analysis methods. The analysis of numerical signals verified the great performance of this method. Finally, synchrosqueezing extracting transform is used to process the bearing vibration signals under variable speed conditions. It has been demonstrated that the proposed method achieves better results than other state-of-art time–frequency analysis methods.