The service conditions of wheelset bearings has a direct impact on the safe operation of railway heavy haul freight trains as the key components. However, speed fluctuation of the trains and few fault samples are the two main problems that restrict the accuracy of bearing fault diagnosis. Therefore, a cross-machine transfer diagnosis (pyDSN) network coupled with interpretable modulated differentiable short-time Fourier transform (STFT) and physics-informed balanced spectrum quality metric is proposed to learn domain-invariant and discriminative features under time-varying speeds. Firstly, due to insufficiency in extracting extract frequency components of time-varying speed signals using fixed windows, a modulated differentiable STFT (MDSTFT) that is interpretable with STFT-informed theoretical support, is proposed to extract the robust time-frequency spectrum (TFS). During training process, multiple windows with different lengths dynamically change. Also, in addition to the classification metric and domain discrepancy metric, we creatively introduce a third kind of metric, referred to as the physics-informed metric, to enhance transferable TFS. A physics-informed balanced spectrum quality (BSQ) regularization loss is devised to guide an optimization direction for MDSTFT and model. With it, not only can model acquire high-quality TFS, but also a physics-restricted domain adaptation network can be also acquired, making it learn real-world physics knowledge, ultimately diminish the domain discrepancy across different datasets. The experiment is conducted in the scenario of migrating from the laboratory datasets to the freight train dataset, indicating that the hybrid-driven pyDSN outperforms existing methods and has practical value.

提出了通过跨机器迁移诊断网络 (pyDSN) 结合可解释的调制可微分短时傅里叶变换 (STFT) 和物理信息平衡频谱质量度量，以学习在时变速度下具有不变性和辨别性的特征的方法来改善铁路重载货车的轮对轴承故障诊断精度。

Interpretable modulated differentiable STFT and physics-informed
  balanced spectrum metric for freight train wheelset bearing cross-machine
  transfer fault diagnosis under speed fluctuations

适应速度波动下的货车轮对轴承跨机械传输故障诊断的可解释调制可微STFT和物理维持平衡频谱度量