Application of multi-source information fusion and graph neural networks in fault localization for complex agricultural machinery systems
This research introduces Multi-Source Graph-Enhanced Fault Localization (MS-GEFL) framework, a dynamic graph model is used to represent the mechanical system and Graph Neural Networks are used to propagate the information related to the fault across different operating conditions to identify the anomalies.