Open AccessResidual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification
Author(s) -
Wenting Wang,
Yaguo Lei,
Tao Yan,
Naipeng Li,
Asoke K. Nandi
Publication year - 2021
Publication title -
journal of dynamics, monitoring and diagnostics
Language(s) - English
Resource type - Journals
eISSN - 2833-650X
pISSN - 2831-5308
DOI - 10.37965/jdmd.v2i2.43
Subject(s) - prognostics , convolution (computer science) , residual , computer science , recurrent neural network , artificial intelligence , deep learning , long short term memory , layer (electronics) , machine learning , degradation (telecommunications) , artificial neural network , data mining , pattern recognition (psychology) , algorithm , telecommunications , chemistry , organic chemistry
Recently, deep learning is widely used in the field of remaining useful life (RUL) prediction. Among various deep learning technologies, recurrent neural network (RNN) and its variant, e.g., long short-term memory (LSTM) network, are gaining more attention because of their capability of capturing temporal dependence. Although the existing RNN-based approaches have demonstrated their RUL prediction effectiveness, they still suffer from the following two limitations: 1) it is difficult for RNN to extract directly degradation features from original monitoring data, and 2) most of the RNN-based prognostics methods are unable to quantify the uncertainty of prediction results. To address the above limitations, this paper proposes a new method named Residual convolution LSTM (RC-LSTM) network. In RC-LSTM, a new ResNet-based convolution LSTM (Res-ConvLSTM) layer is stacked with convolution LSTM (ConvLSTM) layer to extract degradation representations from monitoring data. Then, predicated on the RUL following a normal distribution, an appropriate output layer is constructed to quantify the uncertainty of the forecast result. Finally, the effectiveness and superiority of RC-LSTM is verified using monitoring data from accelerated degradation tests of rolling element bearings.