Abstract: This paper proposes multi-source domain generalization fault diagnosis method based on instance-level prompt generation to enhance the model's fault recognition capability in cross-domain environments.This method employs a cross-frequency aligned prompt generator to dynamically generate instance-level prompts,enabling refined modeling of local features across diffe-rent samples.It incorporates a semantic consistency enhancement module to ensure the semantic validity of instance-level prompts.Furthermore,to improve the model's stability and adaptability in cross-domain tasks,a memory bank-enhanced contrastive learning module is introduced,which fully utilizes cross-domain positive and negative samples.By storing and dynamically updating feature representations of training samples,this module expands the diversity of positive and negative sample distributions and enhances the effectiveness of cross-domain feature learning.Additionally,a FourierMix module is adopted to perform frequency-domain feature mixing of samples from different source domains,dynamically generating simulated samples to strengthen the model's adaptability on unseen target domains.Experimental results on CWRU and Paderborn datasets demonstrate that the proposed method outperforms existing approaches across multiple unseen target domains,achieving average classification accuracies of 93.54%(1.52% improvement over state-of-the-art) on CWRU dataset and 90.52% (1.30% improvement) on Paderborn dataset.Experimental results prove its effectiveness and robustness in industrial fault diagnosis tasks.

Key words: Fault diagnosis, Prompt learning, Multi-source domain generalization, Transfer learning, Contrastive learning