Generalized S-Synchroextracting Transform for Fault Diagnosis in Rolling Bearing

The vibration signals produced by rotating machinery are mostly non-stationary, and there are numerous methods for dealing with them. People's expectations for time-frequency analysis (TFA) results are increasing all the time. The emergence of post-processing algorithms based on the short-time Fourier transform (STFT) provides scholars with new ideas, but such algorithms heavily rely on the window length selected by STFT and have significant uncertainty. To address this issue, we propose the generalized S-synchroextracting transform, a new time-frequency post-processing algorithm (GS-SET). The algorithm extracts the coefficients on the TF ridge of the generalized S-transform (GST) to remove the majority of the dispersed TF energy, allowing the time-frequency representation (TFR) to achieve optimal TF resolution. The results of the analog signal processing show that the method can characterize the signal clearly and accurately, and it has good noise robustness. To process the fault signals of the three groups of rolling bearings, we use different TFA methods. The results show that the method can more precisely determine the characteristic frequency of the faulty bearing. Finally, the superiority of this method is demonstrated further by processing data from Case Western Reserve University's faulty bearing database.

Automated summary

In this study, a new time-frequency post-processing algorithm called generalized S-synchroextracting transform (GS-SET) is proposed to optimize the resolution of time-frequency representation. Through processing of both simulated signals and fault signals, it is demonstrated that the method has good accuracy and noise robustness.