PCASTNet: A Physics-Constrained Adaptive Style Transfer Network for Sample Generation in Cross-Machine Small-Sample Fault Diagnosis

To compensate for the overlooked data realism and physical consistency in traditional cross-machine sample generation methods, this paper proposes a Physics-Constrained Adaptive Style Transfer Network for Sample Generation in Cross-Machine Small-Sample Fault Diagnosis (PCASTNet) for generating diagnostic samples of the monitored machine under small-sample conditions. PCASTNet employs style transfer to decouple fault content from a reference machine and machine style from the monitored machine. Moreover, it further integrates physical priors by introducing a band energy preservation constraint during sample synthesis. Built upon multi-scale wavelet transform, the framework consists of a wavelet encoder, an Adaptive Style Normalization (AdaSN) module, a wavelet decoder, and a multi-objective loss that jointly constrains content fidelity, style consistency, and energy preservation. Experimental results on two cross-machine scenarios demonstrate that PCASTNet can generate a large number of samples from limited monitored machine data and significantly improves diagnostic accuracy under small-sample conditions.