Fault Diagnosis of Synchronous Motor Based on PSA Guided Double Branch Neural Network Feature Fusion
Targeting the problem of poor accuracy of single sensor signal in synchronous motor fault diagnosis,the paper proposes a pyramid split attention(PSA)based neural network model.Firstly,the three-phase current signal and vibration signal are input into the convolutional neural network as two branches for feature extraction,and the extracted signal features are fused through the feature fusion layer.Secondly,the spatial information of different scales is captured with PSA attention mechanism to enrich the feature space.Finally,the diagnosis results are output through the output layer.The experiments show that the proposed model can significantly improve the accuracy of the synchronous motor fault diagnosis.