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

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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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Acknowledgements

This work is supported by the National Key R&D Program of China (No. 2018YFB1004804), National Natural Science Foundation of China (No. 61702492), Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence, and Shenzhen Basic Research Program (No. JCYJ20170818153016513).

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Authors and Affiliations

  1. Shengzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shengzhen, China

    Wensi Yang & Kejiang Ye

  2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

    Qingfeng Yao

  3. Department of Computer and Information Science, University of Macau, Macau, China

    Cheng-Zhong Xu

  4. University of Chinese Academy of Sciences, Beijing, China

    Wensi Yang & Qingfeng Yao

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  1. Wensi Yang
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  2. Qingfeng Yao
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  4. Cheng-Zhong Xu
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Correspondence to Kejiang Ye.

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Yang, W., Yao, Q., Ye, K. et al. Empirical Mode Decomposition and Temporal Convolutional Networks for Remaining Useful Life Estimation. Int J Parallel Prog 48, 61–79 (2020). https://doi.org/10.1007/s10766-019-00650-1

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