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

• URL: http://arxiv.org/abs/2512.24679v1
• Date: Wed, 31 Dec 2025 07:10:32 GMT
• Title: Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions
• Authors: Pengcheng Xia, Yixiang Huang, Chengjin Qin, Chengliang Liu,
• Abstract summary: This paper proposes a multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis.<n>Experiments are conducted on induction motor fault diagnosis under both unseen constant and time-varying working conditions.
• Score: 7.135832969368086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: https://github.com/xiapc1996/MMDG.

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