Abstract:
Abstract: With the rapid development of electric vehicles， the reliability and safety of charging facilities have become the key factors to ensure user safety and enhance user experience. In view of the limitation that traditional fault diagnosis methods are not suitable for complex fault diagnosis due to insufficient data annotaion， this paper proposes a complex fault diagnosis method of charging facilities based on Bi-LSTM （Bidirectional Long Short-Term Memory Network） residual network from the perspective of maintenance work orders and historical operation work orders of charging facilities. This method extracts the effective features from the original orders and work orders， fuses the time series features of Bi-LSTM with the original features by using residual network， and finally inputs the fused features into the machine learning model to realize the complex fault diagnosis task of charging facilities. Experimental results show that this method has obvious improvement effect on complex fault diagnosis tasks compared with machine learning model.

Key words: Key words: charging facilities, fault diagnosis, Bi-LSTM, residual network, machine learning