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An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme

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  • Yu, Wennian
  • Kim, II Yong
  • Mechefske, Chris

Abstract

Remaining useful life (RUL) estimation of a degrading system is the major prognostic activity in many industry applications. This paper presents an improved version of the similarity-based curve matching method for the remaining useful life estimation of a mechanical system, which is a companion paper of our previous work on RUL estimations using a bidirectional recurrent neural network (RNN) based autoencoder scheme. We propose a zero-centering rule to tackle the varying initial health across instances (systems) when using the similarity-based health index curve matching technique to identify the training instances that share a similar degradation pattern with the test instance whose RUL needs to be determined. However, this rule will also induce a significant prediction error, especially when the off-line training instances are abundant, or the true RULs of the on-line test instances are large. Thus, an ensemble approach that integrates the RUL estimations obtained from the similarity-based curve matching techniques, with and without the zero-centering rules, is introduced to increase the robustness and accuracy of proposed method for RUL estimations. We evaluate the prognostic performance of the ensemble algorithm and standalone algorithms on four publicly available turbofan engine degradation datasets. The results demonstrate that the proposed ensemble approach gives more robust and reliable RUL estimations compared to any independent algorithm used on all the studied datasets.

Suggested Citation

  • Yu, Wennian & Kim, II Yong & Mechefske, Chris, 2020. "An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
  • Handle: RePEc:eee:reensy:v:199:y:2020:i:c:s0951832019307902
    DOI: 10.1016/j.ress.2020.106926
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    References listed on IDEAS

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    1. Listou Ellefsen, André & Bjørlykhaug, Emil & Æsøy, Vilmar & Ushakov, Sergey & Zhang, Houxiang, 2019. "Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 240-251.
    2. Liu, Yingchao & Hu, Xiaofeng & Zhang, Wenjuan, 2019. "Remaining useful life prediction based on health index similarity," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 502-510.
    3. 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.
    4. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
    5. 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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