超图对比传感器融合用于感应电机多模态故障诊断
Hypergraph Contrastive Sensor Fusion for Multimodal Fault Diagnosis in Induction Motors
AI总结：
针对感应电机多模态故障诊断，提出多模态超图对比注意力网络（MM-HCAN），首次在超图拓扑中集成对比学习，实现多故障联合诊断，在三个基准上达到99.82%准确率。
摘要：
可靠的感应电机（IM）故障诊断对于工业安全和运营连续性至关重要，可减少代价高昂的计划外停机。传统方法通常难以捕捉复杂的多模态信号关系，局限于单模态数据或单一故障类型，并且在噪声或跨域条件下性能下降。本文提出多模态超图对比注意力网络（MM-HCAN），一个用于鲁棒故障诊断的统一框架。据我们所知，MM-HCAN是首个在专为多模态传感器融合设计的超图拓扑中集成对比学习的方法，能够联合建模模态内和模态间的依赖关系，并增强超越欧几里得嵌入空间的泛化能力。该模型支持轴承、定子和转子故障的同时诊断，满足工程上对综合诊断能力的需求。在三个真实世界基准上的评估中，MM-HCAN实现了高达99.82%的准确率，具有强大的跨域泛化能力和对噪声的鲁棒性，证明了其在实际部署中的适用性。消融研究验证了每个组件的贡献。MM-HCAN为全面的多故障诊断提供了可扩展且鲁棒的解决方案，支持工业环境中的预测性维护和延长资产寿命。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
