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A prognosis model for wear prediction based on oil-based monitoring

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  • W Wang

    (University of Salford
    Harbin Institute of Technology)

Abstract

This paper reports on the development of a wear prediction model based on stochastic filtering and hidden Markov theory. It is assumed that observations at discrete time points are available such as metal concentrations from oil-based monitoring, which are related to the true underlying state of the system which is unobservable. The system state is represented by a generic term of wear which is modelled by a continuous hidden Markov Chain using a Beta distribution. We formulated a recursive model to predict the current and future system state given past observed monitoring information to date. The model is useful to wear-based monitoring such as oil analysis. Numerical examples are presented in the paper based on simulated and real data.

Suggested Citation

  • W Wang, 2007. "A prognosis model for wear prediction based on oil-based monitoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 887-893, July.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:7:d:10.1057_palgrave.jors.2602185
    DOI: 10.1057/palgrave.jors.2602185
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    References listed on IDEAS

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    1. Gong, Linguo & Tang, Kwei, 1997. "Monitoring machine operations using on-line sensors," European Journal of Operational Research, Elsevier, vol. 96(3), pages 479-492, February.
    2. Kumar, Dhananjay & Westberg, Ulf, 1997. "Maintenance scheduling under age replacement policy using proportional hazards model and TTT-plotting," European Journal of Operational Research, Elsevier, vol. 99(3), pages 507-515, June.
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    Cited by:

    1. Pan, Yan & Liang, Bin & Yang, Lei & Liu, Houde & Wu, Tonghai & Wang, Shuo, 2024. "Spatial-temporal modeling of oil condition monitoring: A review," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    2. Vališ, David & Žák, Libor & Pokora, Ondřej & Lánský, Petr, 2016. "Perspective analysis outcomes of selected tribodiagnostic data used as input for condition based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 231-242.
    3. Riku-Pekka Nikula & Konsta Karioja & Kauko Leiviskä & Esko Juuso, 2019. "Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1563-1579, April.
    4. 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.
    5. David Vališ & Libor Žák & Ondřej Pokora, 2015. "Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data," Journal of Risk and Reliability, , vol. 229(1), pages 36-45, February.
    6. 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.
    7. Zhou, Zhi-Jie & Hu, Chang-Hua & Xu, Dong-Ling & Chen, Mao-Yin & Zhou, Dong-Hua, 2010. "A model for real-time failure prognosis based on hidden Markov model and belief rule base," European Journal of Operational Research, Elsevier, vol. 207(1), pages 269-283, November.
    8. Awat Ghomghaleh & Reza Khaloukakaie & Mohammad Ataei & Abbas Barabadi & Ali Nouri Qarahasanlou & Omeid Rahmani & Amin Beiranvand Pour, 2020. "Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-16, July.

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