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International Journal of Parallel Programming
Published in: International Journal of Parallel Programming 1/2020

14-11-2019

Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation

Authors: Wensi Yang, Qingfeng Yao, Kejiang Ye, Cheng-Zhong Xu

Published in: International Journal of Parallel Programming | Issue 1/2020

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Abstract

Remaining useful life (RUL) prediction plays an important role in guaranteeing safe operation and reducing maintenance cost in modern industry. In this paper, we present a novel deep learning method for RUL estimation based on time empirical mode decomposition (EMD) and temporal convolutional networks (TCN). The proposed framework can effectively reveal the non-stationary characteristics of bearing degradation signals and acquire time-series degradation signals which namely intrinsic mode functions through empirical mode decomposition. Furthermore, the feature information is used as the input to convolution layer and trained by TCN to predict remaining useful life. The proposed EMD–TCN model structure maintains a superior result compared to several state-of-the-art convolutional algorithms on public data sets. Experimental results show that the average score of EMD–TCN model is improved by 10–20% than traditional convolutional algorithms.
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Metadata
Title
Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation
Authors
Wensi Yang
Qingfeng Yao
Kejiang Ye
Cheng-Zhong Xu
Publication date
14-11-2019
Publisher
Springer US
Published in
International Journal of Parallel Programming / Issue 1/2020
Print ISSN: 0885-7458
Electronic ISSN: 1573-7640
DOI
https://doi.org/10.1007/s10766-019-00650-1

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