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Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach

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  • Zhongzhe Chen

    (School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China)

  • Shuchen Cao

    (Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA)

  • Zijian Mao

    (School of Mechanical and Electrical Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China)

Abstract

As the main power source for aircrafts, the reliability of an aero engine is critical for ensuring the safety of aircrafts. Prognostics and health management (PHM) on an aero engine can not only improve its safety, maintenance strategy and availability, but also reduce its operation and maintenance costs. Residual useful life (RUL) estimation is a key technology in the research of PHM. According to monitored performance data from the engine’s different positions, how to estimate RUL of an aircraft engine by utilizing these data is a challenge for ensuring the engine integrity and safety. In this paper, a framework for RUL estimation of an aircraft engine is proposed by using the whole lifecycle data and performance-deteriorated parameter data without failures based on the theory of similarity and supporting vector machine (SVM). Moreover, a new state of health indicator is introduced for the aircraft engine based on the preprocessing of raw data. Finally, the proposed method is validated by using 2008 PHM data challenge competition data, which shows its effectiveness and practicality.

Suggested Citation

  • Zhongzhe Chen & Shuchen Cao & Zijian Mao, 2017. "Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach," Energies, MDPI, vol. 11(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:28-:d:124188
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    References listed on IDEAS

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    1. Le Son, Khanh & Fouladirad, Mitra & Barros, Anne & Levrat, Eric & Iung, Benoît, 2013. "Remaining useful life estimation based on stochastic deterioration models: A comparative study," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 165-175.
    2. Peng, Weiwen & Shen, Lijuan & Shen, Yan & Sun, Qiuzhuang, 2018. "Reliability analysis of repairable systems with recurrent misuse-induced failures and normal-operation failures," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 87-98.
    3. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    4. Zhu, Shun-Peng & Huang, Hong-Zhong & Peng, Weiwen & Wang, Hai-Kun & Mahadevan, Sankaran, 2016. "Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 1-12.
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    Cited by:

    1. Zhongzhe Chen & Baqiao Liu & Xiaogang Yan & Hongquan Yang, 2019. "An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition," Energies, MDPI, vol. 12(16), pages 1-12, August.
    2. Chen, Jiaxian & Li, Dongpeng & Huang, Ruyi & Chen, Zhuyun & Li, Weihua, 2023. "Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. 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.

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