Hypergraph Contrastive Sensor Fusion for Multimodal Fault Diagnosis in Induction Motors
This addresses the problem of reliable and comprehensive fault diagnosis for industrial maintenance engineers, offering a scalable solution for multiple fault types, though it appears incremental in integrating existing techniques like contrastive learning and hypergraphs.
The paper tackled robust multimodal fault diagnosis in induction motors by proposing the Multimodal Hypergraph Contrastive Attention Network (MM-HCAN), achieving up to 99.82% accuracy with strong cross-domain generalization and noise resilience on real-world benchmarks.
Reliable induction motor (IM) fault diagnosis is vital for industrial safety and operational continuity, mitigating costly unplanned downtime. Conventional approaches often struggle to capture complex multimodal signal relationships, are constrained to unimodal data or single fault types, and exhibit performance degradation under noisy or cross-domain conditions. This paper proposes the Multimodal Hypergraph Contrastive Attention Network (MM-HCAN), a unified framework for robust fault diagnosis. To the best of our knowledge, MM-HCAN is the first to integrate contrastive learning within a hypergraph topology specifically designed for multimodal sensor fusion, enabling the joint modelling of intra- and inter-modal dependencies and enhancing generalisation beyond Euclidean embedding spaces. The model facilitates simultaneous diagnosis of bearing, stator, and rotor faults, addressing the engineering need for consolidated di- agnostic capabilities. Evaluated on three real-world benchmarks, MM-HCAN achieves up to 99.82% accuracy with strong cross-domain generalisation and resilience to noise, demonstrating its suitability for real-world deployment. An ablation study validates the contribution of each component. MM-HCAN provides a scalable and robust solution for comprehensive multi-fault diagnosis, supporting predictive maintenance and extended asset longevity in industrial environments.