Life extension has for a long time been an important and highly discussed issue in nuclear and aviation industries, and has recently attracted considerable attention in the subsea oil and gas industry. Decision-making related to life extension is a multidisciplinary problem, but it primarily depends on the remaining useful life. This paper clarifies the concepts of remaining useful life and technical health, and discusses various influencing factors. An overall model with the capability to handle a heterogeneous combination of requirements, such as degradation modelling, uncertain environmental and operational conditions, uncertain sensor data and expert opinion is suggested for life extension decision-making. It is concluded that a physics-based modelling approach is appropriate for equipment in the subsea oil and gas industry.
MarcusB.Condition Based System Technology – where is development heading?In Proceedings of the 17th European Maintenance Congress (Euro Maintenance), Spain, 2004.
Condition Monitoring and Diagnostics of Machines Prognostics Part 1: ISO 13381-1 General Guidelines, 2004 (International Organization for Standardization, Geneva).
4.
JardineA. K. S.DamingL.BanjevikD.A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Systems Signal Proc., 2006, 20, 1483–2510.
5.
FarrarC. R.LievenN. A. J.Damage prognosis: The future of structural health monitoring. Philos. Trans. Royal Soc., 2006, 365, 623–632.
6.
VaidyaP.RausandM.Life extension of machinery in the oil and gas industry. In Proceedings of European Safety and Reliability Conference ESREL, Praha, 2009.
7.
VaidyaP.RausandM.Technical health – in the context of Condition Based Maintenance. In Proceedings of European Safety and Reliability Conference ESREL, Greece2010.
SingpurwallaN. D.WilsonS. P.The Exponential Formula of Reliability and Survival: Does it Always Hold?Lifetime Data Analysis, vol. 1, 1995, pp. 187–194 (Springer, The Netherlands).
23.
KumarD.WestbergU.Proportional hazards modelling of time dependent covariates using linear regression. IEEE Trans. Reliability, 1996, 45, 386–392.
24.
VolkP. J.CoetzeeJ. L.BanjJevicD.JardineA. K. S.Optimal component replacement decision using vibration monitoring and PHM. J. Operat. Res. Soc, 2002, 53(2), 193–202.
25.
WangW.A model to predict the residual life of rolling element bearings given monitored condition monitoring information to date. IMA J. Managem. Math., 2002, 13, 3–16.
26.
NoortwijkJ. M. V.A survey of the application of gamma processes in maintenance. Reliability Engng Syst. Safety, 2009, 94(1), 2–21.
DieulleL.BerenguerA.GrallA.RoussignolM.Sequential condition based maintenance scheduling for a deteriorating system. Eur. J. Operat. Res., 2003, 150(2), 451–461.
29.
LawlessJ.CrowderM.Covariates and random effects in a gamma process model with application to degradation and failure. Lifetime Data Anal., 2004, 10(3), 213–222.
30.
DieulleL.BerenguerC.GrallA.RoussignolM.Asymptotic failure rate of a continuously monitored system. Reliability Engng Syst. Safety, 2006, 91(2), 126–130.
31.
KallenM. J.Van NoortwijkJ. M.Optimal periodic inspection of a deteriorating process with sequential condition states. Int. J. Pressure Vessels Piping, 2006, 83, 249–255.
32.
PandeyM. D.YuanX. X.NoortwqkJ. M.A comparison of probabilistic deterioration models for lifecycle management of structures. Struct. Infrastruct. Engng, 2009, 5(2), 145–156.
WhitemoreG. A.SchenkelbergF.Modelling accelerated degradation data using Weiner diffusion with a time scale transformation. Lifetime Data Anal, 1997, 3, 27–45.
35.
DoksumK. A.HøylandA.Models for variable-stress accelerated life testing experiments based on Weiner processes and inverse Gaussian distribution. Technometrics, 1992, 34, 74–82.
36.
NewbyM.BarkerC. T.A bivariate process model for maintenance and inspection planning. Int. J. Pressure Vessels Piping, 2006, 83(4), 270–275.
RevuzD.YorM.Continuous Martingales and Brownian Motion – A Series ofComprehensive Studies in Mathematics, 3rd edition, vol. 293, 1999 (Springer-Verlag, Berlin).
39.
SiddiqueA.YadavaG. S.SingB.Applications of artificial intelligence techniques for induction machine stator fault diagnosis review. In Proceedings of IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, NY, pp. 29–34, 2003.
40.
BaruahP.ChinnamR. B.HMM for diagnostic and prognostics in maintenance process. Int. J. Production Res, 2005, 43(6), 1275–1293.
41.
LinD.MakisV.Filters and parameter estimation for a partially observable system subject to random failure with continuous range observations. Adv. Appl. Probabil., 2004, 36, 1212–1230.
GertlerJ. J.Fault Detection and Diagnosis in Engineering Systems, 1998 (Marcel Dekker Inc, Basel Switzerland).
44.
FrankP. M.Fault diagnosis in dynamic systems using analytical and knowledge based redundancy – A survey and some new results. Automatica, 1990, 26, 459–474.
45.
WilliamsJ. H.DavisA.DrakeP. R.Condition Based Maintenance and Machine Diagnostics, 1994 (Chapman and Hall, London).
46.
HowardI.JiaS.WangJ.The dynamic modelling of a spur gear in mesh including friction and a crack. Mech. Syst. Signal Proc., 2001, 15, 831–838.
47.
BartelmusW.Diagnostic information on gearbox condition for Techtronic systems. Trans. Inst. Measurement Control, 2003, 25, 451–465.
48.
LoparoK. A.AdamsM. A.LinW.Fault detection and diagnosis of rotating machinery. IEEE Trans. Industrial Electronics, 2000, 47, 1005–1014.
49.
KurfessY.LiT. R.LiangS. Y.Stochastic prognostics for rolling elements bearing. Mech. Syst. Signal Proc, 2000, 14, 747–762.
50.
OppenheimerC. H.LoparoK. A.Physically based diagnosis and prognosis of cracked rotor shaft. In Proceedings of Component and Systems Diagnostics, Prognostics, Health Management II, 4733-Bellingham, 2002, pp. 122–132.
51.
KozinF.BogdanoffJ. L.Probabilistic models of fatigue crack growth: Results and speculations. Nuclear EngngDesign, 1989, 115, 143–171.
52.
BigerelleM.IostM.Bootstrap analysis of FCGR, application to Paris relationship and to life prediction. Int. J. Fatigue, 1999, 21, 299–307.
53.
WalkerJ. R.A probabilistic approach to leak before break in Candu pressure tubes. Int. J. Pressure Vessel Piping, 1990, 43(1–3), 229–239.
54.
SainaT.Chandra KishenJ. M.Probabilistic assessment of fatigue crack growth in concrete. Int. J. Fatigue, 2008, 30(12), 2156–2164.
55.
MayerM. A.BookerJ. M.Eliciting and Analyzing Expert Judgment a Practical Guide, 1991 (Academic Press, Harcourt Brace Jovanovich Publishers, UK).
56.
CookR.Experts in Uncertainty – Opinion and Subjective Probability in Science, 1991 (Oxford University Press, Oxford, UK).
57.
NoortwijkV. J. M.DekkerR.CookR. M.MazzuchiT. A.Expert judgment in maintenance optimization. IEEE Trans. Reliability, 1992, 41, 427–432.
58.
PeresF.BouzaieneL.BocquetJ. C.BillyF.LannoyA.HaikP.Anticipating aging failure using feedback data and expert judgment. Reliability Engng Syst. Safety, 2007, 92, 200–210.
59.
KaplanS.Expert information versus expert opinions, another approach to problem of eliciting/combining/ using expert knowledge in PRA. Reliability Engng Syst. Safety, 1992, 39, 61–72.
