Time-reassigned Multisynchrosqueezing Transform for Bearing Fault Diagnosis of Rotating Machinery

The impulse features in a condition monitoring (CM) signal usually imply the occurrence of a defect in a rotating machine. To accurately capture the impulse components in a CM signal, a concentrated time-frequency analysis (TFA) method based on time-reassigned synchrosqueezing transform (TSST) is proposed. Firstly, the limitation of the TSST method in dealing with strong frequency-varying signals is explored. Secondly, an iteration procedure is introduced to address the blurry time frequency representation problem of TSST. The convergence of the iteration algorithm is also analyzed. Finally, an algorithm is proposed to extract the impulse features for signal reconstructions, which are also useful for an accurate diagnosis of the fault type. A simulated noise-contaminated signal and three sets of experimental data are employed in the study to evaluate the performance of the proposed method. Results obtained from this study confirm that the proposed method has a better performance in dealing with impulsive-like signals than other TFA methods.