Remaining electrical life prediction of AC contactor based on CAE-BiGRU-Attention

Abstract

To tackle the challenges of low prediction accuracy caused by single-feature modeling, and the hidden state of the neural network easily loses some information of the long time series, a method for predicting the remaining electrical life of AC contactor using a convolutional autoencoder-bidirectional gated recurrent unit-attention (CAE-BiGRU-Attention) was proposed in this work. Firstly, the feature parameters were extracted from the AC contactor full-life test, and an optimal feature subset was selected using neighborhood component analysis and Spearman rank correlation coefficient to characterize the degradation state of electrical life effectively. Then, the deep information of the optimal feature subset was extracted using CAE. Finally, the remaining electrical life of the AC contactor was treated as a long time series problem and predicted in time series by BiGRU-Attention accurately. The case analysis demonstrates that the model has better prediction accuracy than recurrent neural network (RNN), long short-term memory (LSTM), GRU, BiGRU and CAE-BiGRU models, with an average effective accuracy of 97.12%. This effectively demonstrates the model’s feasibility to accurately predict temporal sequences in the remaining electrical life prediction of electrical equipment.