IDSN: A one-stage interpretable and differentiable STFT domain adaptation network for traction motor of high-speed trains cross-machine diagnosis

Abstract

A surge of transfer fault diagnosis techniques has been proposed to guarantee the safe operation of traction motor systems. However, existing efforts highly depend on the availability of fault data in source domain, which is rare in practice due to the regular maintenance. Fortunately, self-customized testbeds provide an opportunity to easily obtain fault data, assuming that the simulated data can be utilized to monitor the real-world traction motor systems via the cross-machine diagnosis method. Besides, current deep learning-based cross-machine fault diagnosis methods suffer from the poor physical interpretability and the troublesome hype-parameter selection. To tackle aforementioned issues, a one-stage Interpretable and Differentiable STFT cross-machine dual-driven adaptation Network (IDSN) is proposed. In IDSN, a new paradigm termed interpretable differentiable STFT layer is devised, where a derivable coefficient is introduced to adjust pivotal parameters of STFT such as window length by the gradient descent. Prominently, it is a plug-and-play module, which can be embedded into the arbitrary typical network without conflict. Besides, a novel adaptive trade-off coefficient is developed to tackle the weight matching of the domain discrepancy metric. Finally, to ensure the reliability and effectiveness of cross-machine diagnosis, a concise yet valid smoothed joint maximum mean discrepancy is proposed, which simultaneously promotes intra-class compactness and inter-class separability. The results of experiments confirm that the proposed IDSN outperforms the state of the art.