Adaptive synchroextracting transform and its application in bearing fault diagnosis

Abstract

Time–frequency analysis methods can be used to characterize the time-varying characteristics of a signal. The postprocessing algorithm further enhances this ability. The synchroextracting transform is a typical postprocessing algorithm that has the advantage of energy aggregation. However, based on a short-time Fourier transform, shortcomings such as a fixed window length and amplitude distortion when processing frequency modulation signals are unavoidable. This paper proposes a time–frequency postprocessing algorithm with high adaptability, which is called the adaptive synchroextracting transform (ASET). The filter window width for the ASET is adaptive and is determined by the instantaneous frequency change rate for the signal. On this basis, the improved extraction operator can be used to achieve a high-resolution time–frequency​ representation. This algorithm can be used to better deal with strong frequency modulation signals and has better noise robustness while allowing for signal reconstruction. The effectiveness and practicability of the proposed algorithm are demonstrated by simulation signals and faulty bearing signals.