Adaptive Multimodal Industrial Fault Diagnosis With Attention-Driven Fusion Boosting Unimodal Performance

Industrial fault diagnosis increasingly benefits from Large Models (LMs), which can handle diverse data sources. Within this context, multimodal approaches, which integrate inputs like vibration, process, and video data, boost accuracy but often assume all modalities are available, which rarely holds in practice due to sensor failures, deployment limits, etc. While unimodal methods are more practical, they often lack sufficient information for complex scenarios. The key challenge is how to effectively fuse multimodal data and transfer that knowledge to enhance unimodal performance when some modalities are missing. This paper proposes an adaptive multimodal fault diagnosis framework that enables bidirectional enhancement between multimodal and unimodal representations. For model construction, a Cross-Fusion Channel Attention (CFCA) module is introduced to align features across modalities, and then a Shared Temporal Attention (STA) module captures sequential dependencies and facilitates representation sharing. A multi-head diagnosis structure is finally used to jointly supervise both multimodal and unimodal branches. For parameter estimation, we develop a similarity-aware and uncertainty-guided gradient modulation strategy to adaptively balance multimodal and unimodal learning, ensuring stable optimization and knowledge transfer. Experiments on the real-world industrial PRONTO dataset demonstrate that our method achieves superior performance and adaptability across various scenarios with varying modality availability.