Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions

Abstract:Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability. However, existing methods suffer significant performance decline in real-world scenarios where models are tested under unseen working conditions, while domain adaptation approaches are limited to their reliance on target domain samples. Moreover, most existing studies rely on single-modal sensing signals, overlooking the complementary nature of multi-modal information for improving model generalization. To address these limitations, this paper proposes a multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis. A dual disentanglement framework is developed to decouple modality-invariant and modality-specific features, as well as domain-invariant and domain-specific representations, enabling both comprehensive multi-modal representation learning and robust domain generalization. A cross-domain mixed fusion strategy is designed to randomly mix modality information across domains for modality and domain diversity augmentation. Furthermore, a triple-modal fusion mechanism is introduced to adaptively integrate multi-modal heterogeneous information. Extensive experiments are conducted on induction motor fault diagnosis under both unseen constant and time-varying working conditions. The results demonstrate that the proposed method consistently outperforms advanced methods and comprehensive ablation studies further verify the effectiveness of each proposed component and multi-modal fusion. The code is available at: this https URL.

Artificial Intelligence (cs.AI); Signal Processing (eess.SP)

arXiv:2512.24679 [cs.AI]

(or arXiv:2512.24679v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2512.24679 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1016/j.ymssp.2026.114693 Focus to learn more DOI(s) linking to related resources

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