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

TL;DR

This paper introduces a novel multi-modal cross-domain fault diagnosis model that employs dual disentanglement and mixed fusion strategies to improve robustness and generalization under unseen working conditions.

Contribution

It proposes a dual disentanglement framework and a cross-domain mixed fusion strategy for multi-modal fault diagnosis, enhancing model robustness and generalization in unseen environments.

Findings

Outperforms existing methods in unseen conditions

Effective multi-modal feature disentanglement demonstrated

Robustness verified through extensive experiments

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.