{"ID":2823648,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.24679","arxiv_id":"2512.24679","title":"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: https://github.com/xiapc1996/MMDG.","short_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 targ...","url_abs":"https://arxiv.org/abs/2512.24679","url_pdf":"https://arxiv.org/pdf/2512.24679v1","authors":"[\"Pengcheng Xia\",\"Yixiang Huang\",\"Chengjin Qin\",\"Chengliang Liu\"]","published":"2025-12-31T07:10:32Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"eess.SP\"]","methods":"[]","has_code":false,"code_links":[{"ID":605514,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2823648,"paper_url":"https://arxiv.org/abs/2512.24679","paper_title":"Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions","repo_url":"https://github.com/xiapc1996/MMDG","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}