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Plant residual time modelling based on observed variables in oil samples

Author

Listed:
  • W Wang

    (University of Salford
    Harbin Institute of Technology)

  • B Hussin

    (Universiti Teknikal Malaysia)

Abstract

This paper presents a model and methodology for estimating the residual time of a plant item. This plant item can be an engine or any complex technical system monitored by a regularly spaced oil analysis programme. Typically in the oil samples taken, two groups of observed variables are available, namely, metal concentrations and variables related to the condition of the lubricant and contaminants. We term the former as internal variables and the latter as external variables. External variables are those that may cause the change of the underlying condition of the plant item and therefore the residual time, while internal variables are those variables that only reflect the residual time but cannot change it. We modelled both variables in an oil-based monitoring case, but the principle can be generalized to other monitoring situations. The main techniques used are stochastic filtering for residual time prediction and the maximum likelihood method for parameters estimation. The model established was fitted to the real data of marine diesel engines monitored by an oil analysis programme and the results are presented.

Suggested Citation

  • W Wang & B Hussin, 2009. "Plant residual time modelling based on observed variables in oil samples," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 789-796, June.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:6:d:10.1057_palgrave.jors.2602621
    DOI: 10.1057/palgrave.jors.2602621
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    References listed on IDEAS

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    1. P J Vlok & J L Coetzee & D Banjevic & A K S Jardine & V Makis, 2002. "Optimal component replacement decisions using vibration monitoring and the proportional-hazards model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(2), pages 193-202, February.
    2. W Wang, 2003. "Modelling condition monitoring intervals: A hybrid of simulation and analytical approaches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(3), pages 273-282, March.
    3. Makis, Viliam & Wu, Jianmou & Gao, Yan, 2006. "An application of DPCA to oil data for CBM modeling," European Journal of Operational Research, Elsevier, vol. 174(1), pages 112-123, October.
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    Cited by:

    1. W Wang, 2011. "Overview of a semi-stochastic filtering approach for residual life estimation with applications in condition based maintenance," Journal of Risk and Reliability, , vol. 225(2), pages 185-197, June.
    2. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    3. 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.
    4. Wang, Wenbin & Hussin, B. & Jefferis, Tim, 2012. "A case study of condition based maintenance modelling based upon the oil analysis data of marine diesel engines using stochastic filtering," International Journal of Production Economics, Elsevier, vol. 136(1), pages 84-92.
    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. 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.

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