Diagnosis of interturn short circuit fault in three-phase asynchronous motor under variable operating conditions
The inconsistent distribution of data in different operating conditions poses a challenge in diagnosing inter-turn short circuit faults in three-phase motors.In this paper,a transfer learning method based on residual-self-attention network is proposed.By embedding self-attention mechanism in the residual network,feature enhancement is achieved.The model is trained using source domain data and then fine-tuned using transfer learning strategies to better adapt to the feature distribution of the target domain.Furthermore,a comparative experiment is designed to investigate the impact of fine-tuning training and embedding self-attention mechanism on the diagnostic performance of the model.The experimental results show that the average accuracy of the proposed method for migration under three different load conditions is 87.5%.Compared to the general residual network accuracy,it has increased by 4.5%.At the same time,the recall rate and F1 score have increased by approximately 10%and 6%respectively.
three-phase asynchronous motorfault diagnosisinter-turn short circuitvariable operating conditionstransfer learning
NETL
NSTL
万方数据
维普
