A Novel Motor Fault Diagnosis Method Based on Adaptive Frequency-Domain Graph and Time-Domain Feature Fusion With GCN-GAT

Fault diagnosis of motor as a critical component in industrial systems plays a vital role in ensuring equipment safety and improving production efficiency. To address the challenge of weak signal characteristics under low rotational speed and load-fluctuation conditions, this paper proposes a multi-modal feature fusion method that integrates time-domain features with frequency-domain graph features and an improved GCN-GAT fault diagnosis model based on Graph Neural Networks (GNN). Firstly, an adaptive K-Nearest Neighbor (KNN) graph construction method is introduced to build graph data based on frequency-domain information. Then, by improving the basic GNN architecture, a novel GCN-GAT model is developed to extract both local and global spatial features of graph nodes, with residual connections incorporated to improve model expressiveness and training stability. Key time-domain features are selected using a Random Forest (RF) algorithm, and an attention-based weighted fusion module is designed to adaptively integrate these time-domain features and frequency-domain graph features, thereby enhancing the model’s adaptability to complex operating conditions. Experimental data were collected on a self-built test platform under normal conditions, mechanical faults of bearing and rotor, and electrical faults of stator and rotor, with load variations at speeds of 450 r/min, 900 r/min and 1350 r/min, while data at 2250 r/min serve as a high rotational speed comparison item. Results demonstrate that the proposed model achieves high accuracy and robustness in motor fault diagnosis under low rotational speed load-fluctuation conditions, consistently exceeding an accuracy of 95%, which confirms the effectiveness and robustness of the proposed fault diagnosis method.