Research Paper#Fault Diagnosis, Domain Adaptation, Multi-modal Learning🔬 ResearchAnalyzed: Jan 3, 2026 08:49
Multi-modal Fault Diagnosis with Dual Disentanglement
Published:Dec 31, 2025 07:10
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of fault diagnosis under unseen working conditions, a crucial problem in real-world applications. It proposes a novel multi-modal approach leveraging dual disentanglement and cross-domain fusion to improve model generalization. The use of multi-modal data and domain adaptation techniques is a significant contribution. The availability of code is also a positive aspect.
Key Takeaways
- •Addresses the performance decline of fault diagnosis models under unseen working conditions.
- •Employs a dual disentanglement framework to separate modality-invariant/specific and domain-invariant/specific features.
- •Utilizes a cross-domain mixed fusion strategy for data augmentation.
- •Integrates multi-modal heterogeneous information through a triple-modal fusion mechanism.
- •Demonstrates superior performance compared to existing methods on induction motor fault diagnosis.
Reference
“The paper proposes a multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis.”