Multi-synchrosqueezing S-transform for fault diagnosis in rolling bearings

Abstract Rolling bearings are one of the most significant components of much large machinery, and also one of the components prone to failure. Advanced time–frequency analysis (TFA) methods can provide time–frequency (TF) graphs with more significant features that are critical for fault diagnosis of rolling bearings. In this paper, we propose a new TF algorithm, called the multi-synchrosqueezing S-transform, in which an S-transform is embedded into a multi-synchrosqueezing framework, by reassigning the TF coefficients of the S-transform result in frequency multiple times to achieve the ideal TFA. Using the Rényi entropies to measure the resolution of the TFA and determine iteration, this method can get a better time–frequency representation (TFR) with fewer iterations. The results show that the algorithm can produce TFRs with higher TFR resolution while inheriting the advantages of the S-transform. Through simulation signals and field signals, the effectiveness of the method is verified.