Gear Fault Diagnosis Method Based on the Optimized Graph Neural Networks

The gear fault diagnosis technology based on the signal is crucial for maintaining the normal operation of the gear in the motor drive chain. In some cases, it is challenging to add sensors on the unit of the motor transmission chain for collecting vibration signals in practical engineering applications. However, the current signal can be collected. Nonetheless, due to the long distance between the collection point and the fault source, it becomes difficult to extract the features of the weak gear fault from the current signal. In order to solve the aforementioned problems efficiently, an optimized principal neighborhood aggregation (OPNA) graph neural network (GNN) was proposed to diagnose gear faults in the motor drive chain. First, the current signal is reconstructed to obtain the topological data graph sample by the graph sample construction method proposed in this article. Second, OPNA, an architecture that combines multiple message aggregators with a degree scaler, was designed to extract the features of nodes and edges. Subsequently, the embedding and the particular pooling improvement were used to reduce the number of nodes and achieve steady and rapid classification. Finally, the experimental studies, based on the current signal of the gear dataset, were conducted to validate the effectiveness of the proposed method and its superiority over the traditional methods.