A target-domain augmentation and class-wise alignment framework for few-shot cross-condition bearing fault diagnosis

Cross-condition bearing fault diagnosis often has to adapt to a new operating condition with only a few labelled target samples. This setting creates two coupled problems. First, the sparse target support set cannot describe the target fault distribution sufficiently. Second, the same fault class may occupy different feature regions across source and target conditions, which weakens source-domain pretrained classifiers. Existing methods usually improve few-shot learning or reduce domain shift, but they rarely enrich the labelled target-class representation before class-wise source-target alignment. To address this limitation, this paper proposes a target-domain augmentation and class-wise alignment framework for few-shot cross-condition bearing fault diagnosis. The rime optimization algorithm (RIME) is first used to optimize the variational mode decomposition (VMD) parameters K and