接触器
计算机科学
特征(语言学)
人工智能
秩相关
卷积神经网络
模式识别(心理学)
循环神经网络
深度学习
人工神经网络
机器学习
语言学
哲学
功率(物理)
物理
量子力学
作者
Chaojian Xing,Shuxin Liu,Shidong Peng,Shuyu Gao,Yang Liu,Jing Li,Yundong Cao
标识
DOI:10.1088/1361-6501/ad05a1
摘要
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.
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