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Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction

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  • Li, Xiang
  • Zhang, Wei
  • Ding, Qian

Abstract

Accurate evaluation of machine degradation during long-time operation is of great importance. With the rapid development of modern industries, physical model is becoming less capable of describing sophisticated systems, and data-driven approaches have been widely developed. This paper proposes a novel intelligent remaining useful life (RUL) prediction method based on deep learning. The time-frequency domain information is explored for prognostics, and multi-scale feature extraction is implemented using convolutional neural networks. Experiments on a popular rolling bearing dataset prepared from the PRONOSTIA platform are carried out to show the effectiveness of the proposed method, and its superiority is demonstrated by the comparisons with other approaches. In general, high accuracy on the RUL prediction is achieved, and the proposed method is promising for industrial applications.

Suggested Citation

  • Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    • Handle: RePEc:eee:reensy:v:182:y:2019:i:c:p:208-218
    DOI: 10.1016/j.ress.2018.11.011
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    References listed on IDEAS

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    1. Ahmed Ragab & Mohamed-Salah Ouali & Soumaya Yacout & Hany Osman, 2016. "Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 943-958, October.
    2. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    3. Jouin, Marine & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine, 2016. "Degradations analysis and aging modeling for health assessment and prognostics of PEMFC," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 78-95.
    4. Zhao, Zeqi & Bin Liang, & Wang, Xueqian & Lu, Weining, 2017. "Remaining useful life prediction of aircraft engine based on degradation pattern learning," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 74-83.
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