Deep time–frequency learning for interpretable weak signal enhancement of rotating machineries

Abstract

Rotating machinery generates periodic impulses when weak faults occur, but the fault resonant bands (FRB) which contains rich fault information is usually seriously contaminated by random interference and background noise. With the development of deep learning (DL), the existing signal enhancement methods based on DL have the property of ”black box”, i.e., limited interpretability, by which the fault components with clear physical meaning (CPM) cannot be extracted. Aiming at the aforementioned problems, an interpretable weak signal enhancement method based on deep time–frequency learning (DTFL) is proposed. Firstly, a series of simulated template signals of different resonant bands with CPM are constructed as training samples for DTFL, besides, a resonant band time–frequency ratio mask with CPM is generated as the training target by using the time–frequency representations (TFR) of pure and noise-added simulated template signals to construct a non-linear mapping relationship between the TFR of simulated template signals and resonant bands, thereby to achieve accurate enhancement of FRB and credible mask suppression of background noise interference. Therefore, the time–frequency ratio mask with CPM obtained from the DTFL model can be adapted to enhance the FRB, which enables the results of the enhanced FRB to be interpretable, thereby the constructed DTFL model is interpretable. The DTFL method overcomes the problem that the existing signal enhancement methods rely heavily on expert experience, which can provide a CPM of the FRB to support the diagnosis of weak faults in rotating machineries.