Heterogeneous Graph-Based Multimodal Fusion for Flat Wire Motor Vibration Testing With Data Anomaly Diagnosis

Flat-wire motors play a vital role in a variety of industrial processes as core components in critical industrial applications. However, precise positioning of sensors during vibration testing is essential to ensure the accuracy of motor condition monitoring. In this study, a heterogeneous graph neural network-based method for diagnosing signal anomalies in vibration sensors is proposed to address the problem of vibration signal uncertainty caused by sensor installation deviations or looseness. The method clarifies the complex relationship between sensor position deviation, looseness, and vibration signals by constructing a meta-path subgraph between sensor nodes. Through the multimodal fusion technique, the anomaly features are effectively aggregated to improve the accuracy of fault detection. Experiments on a 106-kW flat-wire motor verified the effectiveness of the proposed method, which significantly improved the reliability and operating efficiency of the motor, especially at 24 N and 2000 r/min, with a diagnostic accuracy of 92%. The proposed method provides a novel solution for the diagnosis of abnormal motor vibration signals in industrial testing.