Prognosis - subsea oil and gas industry



P. Vaidya


Life extension has been an important and highly discussed issue in nuclear and aviation industries for a long time and it has recently attracted a considerable attention in subsea oil and gas industry. Decision regarding life extension is primarily based on the remaining useful life. The paper explains the technical health and the other factors that influence the remaining useful life. Degradation mechanisms and the lifetime models are discussed, highlighting the limitations of classical approach and the need for Bayesian approach. A model to predict remaining useful life needs to have a capability of handling heterogeneous combination of requirements like degradation modelling, uncertain sensor data handling, and incorporating expert opinion. The paper explores the suitability of using Bayesian Belief Network as a modelling tool for such prediction in subsea oil and gas industry.

How to Cite

Vaidya, P. (2010). Prognosis - subsea oil and gas industry. Annual Conference of the PHM Society, 2(1).
Abstract 24 | PDF Downloads 31



prognosis, Subsea, RUL

Altamiranda, E., Kiaer, L. & Hu, X. (2009) Condition Monitoring and Diagnosis for Subsea Control Systems: A subsystem prototype, OCEANS- EUROPE’ 09
Banjevic, D. & Jardine, A. K. S. (2006) Calculation of reliability function and remaining useful life for a Markov failure time process , IMA journal of management mathematics, 17(2), 800-8005
Bolch, H. P. & Geiter, F. K. (1994) An Introduction to Machinery Reliability Assessment, Gulf Publishing Company: Houston, TX
Boudali, H. & Dugan J. B. (2005) A discrete time Bayesian Network reliability modelling and analysis framework, Reliability engineering and system Safety, 87(3), 337-349
Bouissou, M., Martin, F. & Ourghanlian, A. (1999) Assessment of safety critical system including software: A Bayesian belief network for evidence sources, Reliability and Maintainability Symposium (RAMS 99) Washington
Bouissou, M. & Nguyen, T. (2002) Decision making based on expert assessments: Use of Belief Networks to take into account uncertainty, bias,and weak signals, 13th European Safety and
Reliability International Conference (ESREL)
Bugaenko, S. E., Arzhaev, A. I., Evropin, S. V. & Savchenko, V. A. (2002) Management of the service life of a nuclear Power Plant, Atomic Energy, 92 (4), 279-286
Celeuex, G., Corset, F., Lannoy, A. & Ricard, B. (2006) Designing a Bayesian network for preventive maintenance from expert opinions in a rapid and reliable way, Reliability Engineering and System Safety, 91(7), 849-856
Charniak, E. (1991) Bayesian networks without tears, AI Mag. 12 (4), 50–63
Van der Gaag, L.C. (1996) Bayesian belief networks: odds and ends, Computer J. 39, 97–113
Chinnam, R.B. & Baruah, P. (2003) Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering, Proceedings of the international joint conference on neural networks, New York 1(4), 2466-2471
Clemen, R.T. & Winkler, R.L. (1999) Combining probability distributions from experts in risk analysis, Risk Anal. 19 (2), 187–203.
Daley, C. P. (1999) Aircraft Update Programmes. The economical alternative?, RTO SCI symposium, Ankara, Turkey, 26-28, published in RTO MP -44, April1999
Farrar, C. R., & Lieven, N. A. J. (2006) Damage prognosis: The future of structural health monitoring, Philosophical Transactions of the Royal Society, 365, 623-632
Peres, F., Bauzaiene, L., Bocuet, J. C., Billy, F., Lannoy, A. & Haik, P. (2007) Anticipating aging failure using feedback data and expert judgment, Reliability Engineering and system safety, 92, 200- 210
Friedemann, J. D., Verma, A., Bonissone, P.,Iyer, N. (2008) Subsea Condition Monitoring – A path to increased availability and increased recovery, SPE 112051
Friis-Hansen, A., Hansen P., Christensen C.F. (2008) Reliability analysis of upheaval buckling updating and cost optimization, 8th ASCE Specialty Conference on Probabilistic mechanics and Structural Reliability, PMC
Goode, K. B, Moore, J. & Roylance, J. (2000) Plant machinery working life prediction method utilizing reliability and condition monitoring data., Proceedings of the Institution of Mechanical Engineers, London 214,109-122
Gelman, A., Carlin, J. B., Stern, H.S. & Rubin, D.B. (1995) Bayesian data Analysis, Texts in Statistical Science, Chapman & Hall, ISBN 0-412-03991-5
Gran, B.A. (2002). Assessment of programmable systems using Bayesian Belief Network, Safety Science, 40,797-812
Haitao, L., Wenbiao, Z. & Huairui, G. (2006) Predicting remaining useful life of an individual unit using proportional hazard model and logistic regression model, IEEE, 1,127-132, 2006
Heckerman, D., Geiger, D. & Chickering, D.M. (1995) Learning Bayesian networks: the combination of knowledge and statistical data, Machine Learning, 20, 197–243
Helminen, A. & Pulkkenien, U. (2003) Reliability Assessment using Bayesian network - Case study on quantitative reliability estimation of a software based motor protection relay, VTT industrial system, STUK-YTO-TR, 198, Helsinki
Horan, C. S., Starky, S. G., Lucas, M. G. (2008) New deepwater subsea equipment qualification system eases execution, improves reliability (SUBSEA), Offshore
ISO 13381-1. (2004) Condition monitoring and diagnostics of machines – Prognostics - Part1: General guidelines, International Organization for Standardization, Geneva
Jardine, A. K. S., Daming, L. & Banjevik, D. (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical systems and signal processing, 20, 1483-2510
Jensen, F. V. (1996). An introduction to Bayesian Networks, UCL Press, London, UK
Jensen, F. V. (2001) Bayesian Networks and Decision Graphs, Springer-V erlag, ISBN 0-387-95259-4, NY
Jensen, F. V. & Nielsen, T. D. (2007) Bayesian Networks and Decision Graphs, Second Edition, Springer Science, ISBN 0-387-68281-3 NY
Kang, C. W., Golay, M.W. (1999) Bayesian belief network based advisory system for operational availability focused diagnosis of complex nuclear power systems, Expert Systems with Applications, 17, 21-32
Karassik, I. J., Joseph, P., Messina, Cooper P., Charls, C., Heald. (2001) Pump Handbook, McGraw Hill, 3rd Edition
Kjaerulff, U. B. & Madsen, A.L. (2008) Bayesian Networks and Influence Diagrams, Springer Science (ISBN 978-0-387-74100-0), NY
Kothamasu, R., Huang, S. H. & William, S. V. (2006) System health monitoring and prognostics – a review of current paradigms and pratices, International Journal of Advanced Manufacturing Technology, 28, 1012-1024
Langseth, H. & Portinale, L. (2007) Bayesian Networks in reliability, Reliability Engineering and System Safety, 92(1), 92-108
Langseth, H. (2008) Bayesian networks in Reliability: The good, the Bad and the Ugly, Advances in mathematical modelling for Reliability, IOS Press, Amsterdam, Netherland
Laurizen, S. L. (1995) The EM algorithm for graphical association models with missing data, Computat. Stat. Data Anal. 19, 191–201
Lindley, D.V. (2007) Understanding Uncertainty, Wiley: Hoboken, NJ.
Pearl, J. (1988) Probabilistic reasoning in intelligent systems: network of plausible inference. Morgan Kaufmann Publishers Inc. San Fransisco, USA
Mahadevan, S., Zhang, R. X. & Smith, N. (2001) ,Bayesian Networks for system reliability reassessment, Structural Safety, 23(3),231-251
Mengshoel, O. J. (2007) Designing resource-bounded reasoners using Bayesian networks: System health monitoring and diagnosis, Proceedings of the 18th International Workshop on Principles of Diagnosis
(DX-07), 330-337, Nashville, TN.
Mengshoel, O. J., Darwiche, A. & Uckun, S. (2008) Sensor validation using Bayesian networks, Proceedings of the ninth international Symposium on Artificial Intelligence, Robotics and Automation in Space (iSAIRAS-08) Los Angelis, CA
Nieuwhof, G. W. E. (1984) The concept of failure in reliability engineering , Reliability Engineering, 7, 53-59
Noortwijk, van, J. M., Dekker, A., Cook, R.M. & Mazzuchi, T. A. (1992) Expert judgment in maintenance optimization, IEEE Transactions on Reliability, 3, 427-432
Norrington, L., Quigley, J., Russel, A., Van der Meer, R. (2007 ) Modeling the reliability of search and rescue operations with Bayesian Belief Networks, Reliability Engineering and System Safety, 93 (7), 940-949.
Nystad, B. H. (2008) Technical Condition Indexes and Remaining Useful Life of Aggregated Systems, PhD Theses, Norwegian University of science and Technology
Rausand, M. & Høyland, A. (2004) System Reliability Theory; Models, Statistical Methods, and Applications, Wiley, Hoboken, NJ, 2nd edition
Reinertsen, R. (1996) Residual life of technical systems; diagnosis, prediction and life estimation, Reliability Engineering and System Safety, 54, 23- 34
Sandsmark, N. & Meheta, S. (2004) Integrated Information Platform for Reservoir and Subsea Production systems, Proceedings of the 13th Product Data Technology – Europe Symposium 2004, Stockholm
Sigurdsson, J. H., Walls, L. A. & Quigley, J. L. (2001) Bayesian belief nets for managing expert judgment and modelling reliability, Quality and Reliability Engineering International, Special issue:14th advances in reliability technology symposium (ARTS), 17(3), 181-190
Singpurwalla, N. D. (1995) Survival in dynamic environments, Statistical Science, 10(1), 86-103
Spiegelhalter, D. J., Dawid, A. P., Laurizen, S. L. & Cowell, R.G. (1993) Bayesian analysis in expert systems, Stat. Sci. 8 (3), 219–247
Status Report on Nuclear Power Plant Life Management-Nuclear Energy Agency Committee for technical and economic studies on Nuclear energy development and fuel Cycle NEA/SEN/NDC/2000
Straub, D. (2009) Stochastic Modelling of Deterioration Processes through Dynamic Bayesian Networks, Journal of Engineering Mechanics, 135 (10), 1089- 1099
Vaidya, P. & Rausand, M. (2009) Life extension of machinery in the oil and gas industry, Proceedings of European Safety and Reliability Conference ESREL, Praha
Vaidya, P. & Rausand, M. (2010) Technical health – in the context of condition based maintenance, Proceedings of European Safety and Reliability Conference ESREL, Crete
Volk, P . J., Wnek, M. & Zygmunt, M. (2004) Utilizing statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions, Mechanical Systems and signal Processing, 18(4), 833-847
Van der Gaag, L. C. (1996) Bayesian belief networks: odds and ends, Computer J. 39, 97–113
Vatn, J. (1996) Maintenance Optimization, Models and Methods, PhD Theses, Norwegian University of science and Technology
Wang, W. & Zang, W. (2008) An asset residual life prediction model based on expert judgments. European Journal of Operational Research, 188(2), 496-505
Wang, W., Scarf, P.A. & Smith, A. J. (2000) On the application of model of condition-based maintenance, Journal of the Operations Research Society, 51,1218-1227
Weber, P. & Jouffe, L. (2006) Complex System Relaibility Modelling with Dynamic Object Oriented Bayesian Networks (DOOBN), Reliability Engineering and System Safety, 91(2), 149-162
Willy, R., Mosleh, A., Vinnem, J. E., & Aven, T. (2009) On the use of hybrid causal logic method in offshore risk analysis, Reliability Engineering and System safety, 94, 445-455
Xue, F., Bonissone, P., Varma, A., Yan, W., Eklund N. & Goebel, K. (2008) An instance –based method for remaining useful life estimation for aircraft engine, Journal of Failure Analysis and Prevention, 8(2), 199-206
Yan, J., Koc, M. & Lee, J. (2004) A prognostic algorithm for machine performance assessment and its application. Production planning and control, 15,796-801
Yongli, Z., Limin, H. & Jinling, L. (2006) Bayesian network based approach for power system fault diagnosis, IEEE Transactions on Power Delivery, 21: 634-639
Note: This paper has extracts from the earlier paper written by the author referred above (Vaidya and Rausand, 2009) and (Vaidya and Rausand, 2010)
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