Multimodal unified generalization and translation network for intelligent fault diagnosis under dynamic environments

Multimodal data fusion can generate reliable fault representations for intelligent fault diagnosis. However, simple data fusion strategies often introduce fault-irrelevant information, thereby reducing robustness against unknown domain shifts. Moreover, traditional methods generally lack adaptive mechanisms to address missing modalities, leading to considerable performance degradation under sensor failure conditions. To address these problems, this paper proposes a multimodal unified generalization and translation network. To learn invariant unified representations for resisting unknown data distribution shifts, information-enhanced concatenation first generates intra-domain and cross-domain representations. Subsequently, mutual information maximization is applied to remove fault-unrelated information from these representations. Finally, A hybrid ensemble diagnosis strategy fully leverages the interaction of multimodal information across different levels. In addition, semantic supervision investigates the relationships among different modalities and enables intermodal translation in the event of a sensor failure within the monitoring system. Extensive experimental results based on a public bearing dataset and a self-collected motor dataset indicate that the proposed method improves accuracy by 10.53 % and 8.47 % compared to the state-of-the-art methods, respectively. The code and datasets are available at https://github.com/CHAOZHAO-1/MUGTN.