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Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data

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  • David ValiÅ¡
  • Libor Žák
  • OndÅ™ej Pokora

Abstract

At present, numerous approaches are devoted to monitoring a system state. Their intention is to determine the current state of a system and predict reliability parameters for the future. This article addresses one of the several possible approaches that allows us to determine a system technical state on the basis of diagnostic data. These diagnostic data are from the area of tribiodagnostics, namely, engine oil. The article examines iron and lead particles that are selected deliberately with respect to their origin in kinematic parts of the system and their degree of correlation with operation measures. The particles occur in oil during both operating time and calendar time development. To model their occurrence during operation time, we have used, in the first part of the article, a mathematical regression method to set parameters. In the second part, we have applied a diffusion model based on a Wiener process. The results confirm that we are able to estimate the residual technical life of a system. Moreover, the results enable us to schedule properly the intervals of preventive maintenance (oil change) and to plan a mission/operation. This results in optimising life cycle costs. It is assumed that the potential of the diagnostic data will be extracted by other approaches and methods. In the subsequent work, it will be useful to determine specific interval values of optimised preventive maintenance.

Suggested Citation

  • David ValiÅ¡ & Libor Žák & OndÅ™ej 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.
    • Handle: RePEc:sae:risrel:v:229:y:2015:i:1:p:36-45
    DOI: 10.1177/1748006X14547789
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    References listed on IDEAS

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    1. 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.
    2. Charles E. Smith & Petr Lánský, 1994. "A reliability application of a mixture of inverse gaussian distributions," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 10(1), pages 61-69.
    3. 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.
    4. Wenbin Wang & Wenjuan Zhang, 2005. "A model to predict the residual life of aircraft engines based upon oil analysis data," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(3), pages 276-284, April.
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    Cited by:

    1. Altun, Mustafa & Comert, Salih Vehbi, 2016. "A change-point based reliability prediction model using field return data," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 175-184.
    2. Wen, Yuxin & Wu, Jianguo & Das, Devashish & Tseng, Tzu-Liang(Bill), 2018. "Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 113-124.

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