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 α, and fault-sensitive components are selected using envelope entropy and adjacent modal-frequency overlap. The reconstructed signal is converted into symmetric dot pattern (SDP) images to provide a noise-suppressed polar representation. The fast generative adversarial network (FastGAN) then generates auxiliary target-domain SDP samples from the limited labelled target support set, making each target fault class less sparse. On this basis, class-wise maximum mean discrepancy (CMMD) is embedded into a Swin Transformer classifier to align source and target features within each known fault category. Experiments on the Case Western Reserve University (CWRU) and Xi'an Jiaotong University-Shenyang Blower Works (XJTU-SY) datasets show that the proposed framework improves target-domain few-shot adaptation, achieving 93.33% and 97.58% accuracy under the G20 and G50 XJTU-SY target-augmentation settings, respectively.