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.

翻译：可靠的感应电机故障诊断对于工业安全和运行连续性至关重要，可有效减少代价高昂的非计划停机。传统方法往往难以捕捉复杂的多模态信号关系，受限于单模态数据或单一故障类型，且在噪声或跨域条件下性能显著下降。本文提出多模态超图对比注意力网络（MM-HCAN），这是一个用于鲁棒故障诊断的统一框架。据我们所知，MM-HCAN首次将对比学习集成于专为多模态传感器融合设计的超图拓扑结构中，能够联合建模模态内与模态间依赖关系，并增强欧几里得嵌入空间之外的泛化能力。该模型可同时诊断轴承、定子和转子故障，满足工程实践中对综合诊断能力的需求。在三个真实基准数据集上的评估表明，MM-HCAN实现了高达99.82%的准确率，同时具备强大的跨域泛化能力和噪声鲁棒性，证明了其在实际部署中的适用性。消融实验验证了各模块的有效性。MM-HCAN为全面多故障诊断提供了可扩展的鲁棒解决方案，有助于工业环境中的预测性维护和设备寿命延长。