  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.
