Multi-modal Fault Diagnosis with Dual Disentanglement
Analysis
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.
“The paper proposes a multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis.”