Prompt-Guided Disentanglement and Fusion Framework for Cross-Domain Fault Diagnosis With Class Mismatch

Most existing cross-domain fault diagnosis methods rely on the assumption of class consistency between source and target domains. However, this assumption is frequently violated in practical industrial scenarios, where varying operating conditions often correspond to inconsistent fault type sets (i.e., class mismatch). To address this challenge, this article proposes a prompt-guided disentanglement and fusion (PGDF) framework, which leverages the inherent semantic structure of natural language to guide the disentanglement of machine states under different operating conditions. Specifically, PGDF constructs learnable prompts combined with domain and class tokens to construct structured joint representations. Furthermore, a cross-modal decomposed fusion module is designed to disentangle linguistic features into independent domain and class factors. Crucially, through a disentanglement-recombination strategy, these factors are fused with observational data, enabling the PGDF to learn expressive features that independently capture domain and class relationships. Extensive experiments on four datasets involving cross-domain, cross-unseen domain, and cross-machine scenarios demonstrate that PGDF significantly outperforms state-of-the-art methods in diagnostic performance.