Adaptive Time-Reassigned Synchrosqueezing Transform for Bearing Fault Diagnosis

In this article, we propose an adaptive time-reassigned synchrosqueezing transform (ATSST) to characterize the signals with strongly time-varying feature. A time-varying window function is used to deduce the ATSST, which achieves a highly concentrated time–frequency representation (TFR) compared with the conventional time-reassigned synchrosqueezing transform (TSST). Meanwhile, the ATSST allows for mode reconstruction with the high accuracy. In this method, we make full use of the Rényi entropy and conduct a time-varying optimal window width selection scheme to evaluate the local window width by an iterative procedure for each time instant. By applying the optimal window width, the TFR can be greatly enhanced. Numerical results of two simulated signals demonstrate the effectiveness of the ATSST in improving the readability of TFR. In addition, two sets of experimental datasets are employed to further evaluate the performance of the ATSST by comparing with some classical and advanced methods. The results indicate the superiority and robustness of the proposed ATSST in the analysis of strongly time-varying signals.