TY - JOUR
AU1 - Liu, Shuxin
AU2 - Gao, Shuyu
AU3 - Peng, Shidong
AU4 - Liu, Yang
AU5 - Li, Jing
AB - AC contactors are used frequently in various low-voltage control lines, so remaining-life prediction for them can significantly improve the operational reliability of power control systems. To address the problem that the existing AC contactor remaining-life prediction methods do not make full use of the correlation between previous and later states in the degradation process, a CNN-GRU (convolutional neural network-gated recurrent unit) method for AC-contactor remaining-life prediction is proposed. Firstly, the entire cycle of an AC contactor’s degradation data is obtained through a whole-life test, from which the characteristic parameters that effectively reflect the operating states of the contactor are extracted; secondly, neighborhood component analysis (NCA) and maximal information coefficient (MIC) are used to eliminate the redundant information of multidimensional parameters in order to select the optimal feature subset; and then, CNN is used to compress the feature dimension and mine the regular information between the features, so as to extract the effective feature vectors; finally, taking the AC contactor remaining electrical life as a long time sequence issue, time-series accurate prediction is performed using GRU. It is verified that this model is better than RNN (recurrent neural network), LSTM (long short-term memory) and GRU models in prediction, with an effective accuracy of 96.63%, which effectively supports the feasibility of time-series prediction in the field of the remaining-life prediction of electrical devices.
TI - Residual-Electrical-Endurance Prediction of AC Contactor Based on CNN-GRU
JF - Machines
DO - 10.3390/machines10111067
DA - 2022-11-11
UR - https://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/residual-electrical-endurance-prediction-of-ac-contactor-based-on-cnn-y070VFCIxp
SP - 1067
VL - 10
IS - 11
DP - DeepDyve
ER -