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Remaining useful lifetime estimation and noisy gamma deterioration process

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  • Le Son, Khanh
  • Fouladirad, Mitra
  • Barros, Anne

Abstract

In many industrial issues where safety, reliability, and availability are considered of first importance, the lifetime prediction is a basic requirement. In this paper, by developing a prognostic probabilistic approach, a remaining lifetime distribution is associated to the system or component under consideration. More particularly, the system׳s deterioration is modelled by a non-homogeneous gamma process. The model considers a noisy observed degradation data and by using the Gibbs sampling technique, the hidden degradation states are approximated and afterwards the system׳s remaining useful lifetime distribution is estimated. Our proposed prognosis method is applied to the Prognostic and Health Management (PHM) 2008 conference challenge data and the interest of our probabilistic model is highlighted. To point out the interest of the prognostic, a maintenance decision rule based on the remaining lifetime estimation results is proposed.

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  • Le Son, Khanh & Fouladirad, Mitra & Barros, Anne, 2016. "Remaining useful lifetime estimation and noisy gamma deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 76-87.
    • Handle: RePEc:eee:reensy:v:149:y:2016:i:c:p:76-87
    DOI: 10.1016/j.ress.2015.12.016
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    1. Dongliang Lu & Mahesh D Pandey & Wei-Chau Xie, 2013. "An efficient method for the estimation of parameters of stochastic gamma process from noisy degradation measurements," Journal of Risk and Reliability, , vol. 227(4), pages 425-433, August.
    2. 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.
    3. Kallen, M.J. & van Noortwijk, J.M., 2005. "Optimal maintenance decisions under imperfect inspection," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 177-185.
    4. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
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    Cited by:

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    4. Hazra, Indranil & Pandey, Mahesh D. & Manzana, Noldainerick, 2020. "Approximate Bayesian computation (ABC) method for estimating parameters of the gamma process using noisy data," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    5. Xia, Tangbin & Dong, Yifan & Xiao, Lei & Du, Shichang & Pan, Ershun & Xi, Lifeng, 2018. "Recent advances in prognostics and health management for advanced manufacturing paradigms," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 255-268.
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    15. Chatenet, Q. & Remy, E. & Gagnon, M. & Fouladirad, M. & Tahan, A.S., 2021. "Modeling cavitation erosion using non-homogeneous gamma process," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    16. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    17. Dong, Qinglai & Cui, Lirong, 2019. "A study on stochastic degradation process models under different types of failure Thresholds," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 202-212.

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