Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

Authors

  • Wenting Wang Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China
  • Yaguo Lei Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China https://orcid.org/0000-0002-5167-1459
  • Tao Yan Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China https://orcid.org/0000-0002-3328-2118
  • Naipeng Li Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, China
  • Asoke Nandi Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge, United Kingdom https://orcid.org/0000-0001-6248-2875

DOI:

https://doi.org/10.37965/jdmd.v2i2.43

Keywords:

deep learning, residual convolution LSTM network, remaining useful life prediction, uncertainty quantification

Abstract

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.

Downloads

Published

2021-12-21

How to Cite

Wang, W., Lei, Y., Yan, T., Li, N., & Nandi, A. (2021). Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification. Journal of Dynamics, Monitoring and Diagnostics, 1(1), 2–8. https://doi.org/10.37965/jdmd.v2i2.43

Issue

Section

Invited Article