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

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.

Keywords

Failure prediction system residual technical life estimation field data Wiener process

Get full access to this article

View all access options for this article.

References

Labeau

Smidts

Swaminathan

. Dynamic reliability: towards an integrated platform for probabilistic risk assessment. Reliab Eng Syst Safe 2000; 68(3): 219–254.

Park

. Condition-based predictive maintenance by multiple logistic function. IEEE T Reliab 1993; 42(4): 556–560.

Maillart

Pollock

. Cost-optimal condition-monitoring for predictive maintenance of 2-phase systems. IEEE T Reliab 2002; 51(3): 322–330.

Yang

. A condition-based failure-prediction and processing-scheme for preventive maintenance. IEEE T Reliab 2003; 52(3): 373–383.

Pham

. An inspection-maintenance model for systems with multiple competing processes. IEEE T Reliab 2005; 54(2): 318–327.

Jardine

AKS

Lin

Banjevic

. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Pr 2006; 20(7): 1483–1510.

Kim

Kolarik

. Real-time condition reliability prediction from on-line tool performance data. Int J Prod Res 1992; 30(8): 1831–1844.

Chinnam

. On-line reliability estimation for individual components using statistical degradation signal models. Qual Reliab Eng Int 2002; 18: 53–73.

Gebraeel

Lawley

. Residual-life distributions from component degradation signals: a Bayesian approach. IIE Trans 2005; 37: 543–557.

10.

Kolarik

. Real-time performance reliability prediction. IEEE T Reliab 2001; 50(4): 353–357.

11.

Chinnam

. On-line reliability estimation of individual components, using degradation signals. IEEE T Reliab 1999; 48(4): 403–412.

12.

Kolarik

. Multivariate performance reliability prediction in real-time. Reliab Eng Syst Safe 2001; 72: 39–45.

13.

Kharoufeh

Cox

. Stochastic models for degradation-based reliability. IIE Trans 2005; 37: 533–542.

14.

Toms

. Machinery oil analysis – a guide for maintenance managers, supervisors and technicians. Park Ridge, IL: Society of Tribologists and Lubrication Engineers, 2008.

15.

Wang

Zhang

. A model to predict the residual life of aircraft engines based on oil analysis data. Nav Res Log 2005; 52: 276–284.

16.

Wang

. A prognosis model for wear prediction based on oil-based monitoring. J Oper Res Soc 2007; 58: 887–893.

17.

Carr

Wang

. A case comparison of a proportional hazards model and a stochastic filter for condition-based maintenance applications using oil-based condition monitoring information. Proc IMechE, Part O: J Risk and Reliability 2008; 222(1): 47–55.

18.

Sun

Mathew

. Mechanical systems hazard estimation using condition monitoring. Mech Syst Signal Pr 2006; 20: 1189–1201.

19.

Wang

Hussin

. Plant residual time modelling based on observed variables in oil samples. J Oper Res Soc 2009; 60: 789–796.

20.

Ghasemi

Yacout

Ouali

. Parameter estimation methods for condition-based maintenance with indirect observations. IEEE T Reliab 2010; 59(2): 426–439.

21.

Ghasemi

Soumaya

Ouali

. Evaluating the reliability function and the mean residual life for equipment with unobservable states. IEEE T Reliab 2010; 59(2): 426–439.

22.

Medjaher

Tobon-Mejia

Zerhouni

. Remaining useful life estimation of critical components with application to rearing. IEEE T Reliab 2012; 61(2): 292–302.

23.

Gebraeel

Pan

. Prognostic degradation models for computing and updating residual life distributions in a time-varying environment. IEEE T Reliab 2008; 57(4): 539–550.

24.

X-S

Wang

C-H

. Remaining useful life estimation based on a nonlinear diffusion degradation process. IEEE T Reliab 2012; 61(1): 50–67.

25.

Kumar

Chaturvedi

. True degradation estimation of industrial equipment with fuzzy sets: a case study. Proc IMechE, Part O: J Risk and Reliability 2010; 223(2): 167–179.

26.

Ghasemi

Hodkiewicz

. Estimating mean residual life for a case study of rail wagon bearings. IEEE T Reliab 2012; 61(3): 719–730.

27.

Doksum

Hóyland

. Models for variable-stress accelerated life testing experiments based on Wiener processes and the inverse Gaussian distribution. Technometrics 1992; 34(1): 74–82.

28.

Vališ

Pietrucha-Urbanik

. Utilization of diffusion processes and fuzzy logic for vulnerability assessment. Eksploatacja i Niezawodnosc [Maint Reliab] 2014; 16(1): 48–55.

29.

Sherif

Smith

. First-passage time distribution of Brownian motion as a reliability model. IEEE T Reliab 1980; 29(5): 425–426.

30.

Smith

Lánský

. A reliability application of a mixture of inverse Gaussian distributions. Appl Stoch Model D A 1994; 10: 61–69.

31.

Tuckwell

. Elementary applications of probability theory. London: Chapman & Hall, 1995.

32.

Cox

Miller

. The theory of stochastic processes. London: Chapman & Hall, 2001.

33.

Oksendal

. Stochastic differential equations – an introduction with applications. London: Springer, 2000.

34.

Kloeden

Platen

Schurz

. Numerical solution of SDE through computer experiments. London: Springer, 2003.

35.

Vališ

Koucký

Žák

. On approaches for non-direct determination of system deterioration. Eksploatacja i Niezawodnosc [Maint Reliab] 2012; 14(1): 33–41.