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Improved trajectory similarity-based approach for turbofan engine prognostics

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Abstract

Trajectory similarity-based prediction (TSBP) is an emerging real-time remaining useful life (RUL) prediction method that has drawn considerable attention in the field of data-driven prognostics. TSBP is fast, and the corresponding model is easy to train. However, TBSP only provides a point estimation of RUL, which is insufficient for some specific prognostic applications. Hence, this study introduces an improved TSBP method to handle the issue of prognostic uncertainty. On the basis of an adaptive kernel density estimation technique and β-criterion, the improved TSBP method not only provides an accurate and precise point prediction of RUL but also specifies the confidence interval of RUL prediction. The capability of obtaining the confidence interval of RUL can enhance the TSBP method for uncertainty management. The effectiveness of the proposed method is validated through two cases studies, which are related to turbofan engine prognostics.

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Abbreviations

\((w_j^ * ,PRL_j^ * )\) :

Pairs of weights and corresponding PRL obtained by preprocessing step

\({\hat f_h}(x)\) :

Kernel density estimator of PDF

K (●):

Kernel function

\((w_k^l,r_k^l(i))\) :

Pairs of weights and PRL of the l-th testing unit derived from the k-th training degradation trajectory in the i-th monitoring cycle

r l* (i):

Expanded one-dimensional samples of PRLs based on corresponding weights

\({\hat r_i}(i)\) :

Traditional RUL prediction of the l-th testing unit in the i-th operating cycle

\([\alpha _1^ - ,\alpha _1^ + ]\) :

Allowable error bound around the point estimation, i.e., α bounds

\(\pi [\hat{f}({r_u})]_{{\alpha ^ - }}^{{\alpha ^ + }}\) :

Total probability mass of non-parametric probability distribution between α bounds

\([r_ - ^l(i),r_ + ^l(i)]\) :

RUL estimation under the β confidence level

σ :

Proposed parameter controlling specific RUL point estimation

\(r_\sigma ^l(i)\) :

Improved RUL point estimation by the proposed method

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Acknowledgments

This study was sponsored by the National Natural Science Foundation of China under Grant No. 51775090 and the China Scholarship Council under Grant No. 201706070061.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China

    Cheng-Geng Huang, Hong-Zhong Huang, Weiwen Peng & Tudi Huang

  2. Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China

    Cheng-Geng Huang, Hong-Zhong Huang, Weiwen Peng & Tudi Huang

Authors
  1. Cheng-Geng Huang
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  2. Hong-Zhong Huang
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  3. Weiwen Peng
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  4. Tudi Huang
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Corresponding author

Correspondence to Hong-Zhong Huang.

Additional information

Recommended by Associate Editor Byeng Dong Youn

Cheng-Geng Huang received his B.S. degree in Information Display and Photoelectric Technology from the University of Electronic Science and Technology of China, Chengdu, China, in 2013. He is pursuing his Ph.D. degree in Mechanical Engineering at the University of Electronic Science and Technology of China. His research interests include machinery condition monitoring and data mining for machinery prognostics and health management.

Hong-Zhong Huang is a Full Professor of the School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China. He is also the Director of the Center for System Reliability and Safety. He has held visiting appointments at several universities in the USA, Canada, and Asia. He has published 280 journal papers and 8 books in the fields of reliability engineering, optimization design, fuzzy set theory, and product development. Prof. Huang is an ISEAM Fellow, a technical committee member of ESRA, a Co-Editor-in-Chief of the International Journal of Reliability and Applications, and an Editorial Board Member of several international journals.

Weiwen Peng received his B.E. degree in Mechanical Design Manufacture and Automation, his M.E. degree in Mechanical Design and Theory, and his Ph.D. degree in Mechanical Engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2009, 2012, and 2015, respectively. He is currently an Associate Professor with the School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China. His research interests include degradation modeling, machinery health prognostics, and Bayesian machine learning in reliability engineering.

Tu-di Huang received his B.S. degree in Mechanical Design, Manufacturing, and automation from the University of Electronic Science and Technology of China, Chengdu, China, in 2017. He is now pursuing his Master’s degree in Mechanical Engineering at the University of Electronic Science and Technology of China. His research interests are mainly about polymorphic reliability.

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Huang, CG., Huang, HZ., Peng, W. et al. Improved trajectory similarity-based approach for turbofan engine prognostics. J Mech Sci Technol 33, 4877–4890 (2019). https://doi.org/10.1007/s12206-019-0928-3

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  • DOI: https://doi.org/10.1007/s12206-019-0928-3

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