Risk-Based Inspection and Maintenance (RBIM) of Power Plants

  • Faisal KhanEmail author
  • Mahmoud Haddara
  • Mohamed Khalifa
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


The present chapter presents the basic concepts associated with Risk-based Inspection and Maintenance (RBIM) philosophy and their application in maintenance planning aiming at controlling power plant equipment degradation. The basic steps of the method are described, such as inspection sampling, inspection planning and maintenance activity selection based on degradation mechanism evolution, risk assessment and optimization of maintenance plan. The method is customized for power plant analysis considering the constraints associated with that application. Two case studies are presented: the first one is related to a pipeline analysis and the second one is a complete analysis of a power-generating unit.


Crack Size Fault Tree Evidence Theory Quantitative Risk Assessment Ultrasonic Inspection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Agarwal H, Renaud EJ, Preston LE et al (2004) Uncertainty quantification using evidence theory in multidisciplinary design optimization. Reliab Eng Syst Saf 85:281–294CrossRefGoogle Scholar
  2. 2.
    Ang A, Tang W (2007) Probability concepts in engineering. Wiley, New YorkGoogle Scholar
  3. 3.
    API 570 (1998) Piping inspection code. inspection, repair, alteration and rerating of in-service piping systems. API publishing Services, WashingtonGoogle Scholar
  4. 4.
    API 581 (2000) Risk based inspection resource document. API publishing Services, WashingtonGoogle Scholar
  5. 5.
    Apeland S, Aven T (1999) Risk based maintenance optimization: foundational issues. Reliab Eng Syst Saf 67:285–292CrossRefGoogle Scholar
  6. 6.
    ASME (1999) Risk Based Inspection (RBI): A risk-based approach to planned plant inspection. Health and Safety Executive—Hazardous Installations Division, CC/TECH/SAFETY/8Google Scholar
  7. 7.
    Ayyub B, Klir JG (2006) Uncertainty modeling and analysis in engineering and the sciences. Chapman and Hall/CRC, New YorkCrossRefGoogle Scholar
  8. 8.
    Baraldi P, Zio E (2008) A combined monte carlo and possibilistic approach to uncertainty propagation in event tree analysis. Risk Anal 28(4):1–17Google Scholar
  9. 9.
    Balkey KP, Art RJ, Bosnk RJ (1998) ASME Risk-Based in service inspection and testing: an outlook to the future. Risk Anal 18(4):407–421CrossRefGoogle Scholar
  10. 10.
    Berens AP, Hovey PW (1981) Evaluation of NDE reliability characterization. Wright-Patterson Air Force Base, DaytonGoogle Scholar
  11. 11.
    Druschel RB, Ozbek M, Pinder G (2006) Application of Dempster-Shafer theory to hydraulic conductivity. In: Paper of the CMWR—XVI, Conference Program. Copenhagen, 18–22 June 2006Google Scholar
  12. 12.
    Ebeling EC (1997) An introduction to reliability and maintainability engineering. McGRAW-HILL, New YorkGoogle Scholar
  13. 13.
    Etkin DS (2000) Worldwide analysis of marine oil spill cleanup cost factors. In: Paper of the Arctic and Marine Oil spill Program Technical Seminar. Environment Canada, June 2000Google Scholar
  14. 14.
    Ferdous R, Khan F, Sadiq R et al (2009) Methodology for computer aided fuzzy fault tree analysis. Process Saf Environ Protect, IChemE 87:217–226CrossRefGoogle Scholar
  15. 15.
    Ferdous R, Khan F, Sadiq R et al (2009) Handling data uncertainties in event tree analysis. Process Saf Environ Protect, IChemE 87:283–292CrossRefGoogle Scholar
  16. 16.
    Ferson S, Hajagos J, Berleant D et al. (2004) Dependence in Dempster-Shafer theory and probability bounds analysis. US: Sandia National Laboratories, New YorkGoogle Scholar
  17. 17.
    Kallen MJ (2002) Risk-based inspection in the process and refining industry. Msc Thesis, University of Delft, DelftGoogle Scholar
  18. 18.
    Kallen MJ, Noortwijk JM (2004) Optimal inspection and replacement decisions for multiple failure modes. In: Paper of the Proceedings of the 7th International Conference on Probabilistic Safety Assessment and Management. Berlin, 14–18 June 2004Google Scholar
  19. 19.
    Khalifa M, Khan F, Haddara M (2009) Optimal selection of non-destructive inspection technique for welded components. Br J Nondestr Test 51(4):192–200Google Scholar
  20. 20.
    Khan FI, Haddara M (2003) Risk-based maintenance (RBM): a quantitative approach for maintenance/inspection scheduling and planning. J Loss Prev Process Indust 16:561–573CrossRefGoogle Scholar
  21. 21.
    Khan FI, Haddara M (2004) Risk-based maintenance (RBM): a new approach for process plant inspection and maintenance. Process Saf Progr 23(4):252–265CrossRefGoogle Scholar
  22. 22.
    Khan FI, Howard R (2007) Statistical approach to inspection planning and integrity assessment. Nondestr Test Cond Monit 49(1):26–36CrossRefGoogle Scholar
  23. 23.
    Khan FI, Abbasi SA (2000) Analytical simulation and PROFAT II: A new methodology and a computer automated tool for fault tree analysis in chemical process industries. J Hazard Mater 75:1–27CrossRefGoogle Scholar
  24. 24.
    Kowaka M, Tsuge H, Akashi M et al (1994) Introduction to life prediction of industrial plant materials: Application of extreme value statistical method for corrosion analysis. Allerton Press, New YorkGoogle Scholar
  25. 25.
    Krishnasamy L, Khan F, Haddara M (2005) Development of a risk-based maintenance (RBM) strategy for a power-generating plant. J Loss Prev Process Indust 18(2):69–81CrossRefGoogle Scholar
  26. 26.
    Lees FP (1996) Loss prevention in the process industries. Butterworths, LondonGoogle Scholar
  27. 27.
    Li H (2007) Hierarchical risk assessment of water supply systems. PhD Thesis, Loughborough University, p 235Google Scholar
  28. 28.
    Misewicz D, Smith AC, Nessim M et al. (2002) Risk based integrity project ranking. In: Paper of the 4th International Pipeline Conference (IPC2002), Calgary, Sept. 29 to 3 Oct 2002Google Scholar
  29. 29.
    Nessim MA, Stephens MJ, Zimmerman TJ (1995) RiskRisk based maintenance planning for offshore pipelines. Proceedings of the Annual Offshore Technology Conference 2:791–800Google Scholar
  30. 30.
    Paris PC, Endogan F (1963) Critical analysis of crack propagation laws. Basic Eng 85:528–534CrossRefGoogle Scholar
  31. 31.
    Pressure Systems Safety Regulations, PSSR (2000) Safety of pressure systems. Approved Code of Practice (ACoP) SI-2000-128. The Health and Safety Executive (HSE)Google Scholar
  32. 32.
    AC R (2002) Non-electric components reliability data. Center for Reliability Assessment, New YorkGoogle Scholar
  33. 33.
    Sadiq R, Saint-Martin E, Kleiner Y (2008) Predicting risk of water quality failures in distribution networks under uncertainties using fault-tree analysis. Urban Water J 5(4):287–304CrossRefGoogle Scholar
  34. 34.
    Thodi PN, Khan FI, Haddara MR (2009) The selection of corrosion prior distributions for risk based integrity modeling. Stoch Environ Res Risk Assess 23:793–809CrossRefMathSciNetGoogle Scholar
  35. 35.
    Vaurio JK (1995) Optimization of test and maintenance intervals based on risk and cost. Reliab Eng Syst Saf 49:23–36CrossRefGoogle Scholar
  36. 36.
    Vesely WE, Goldberg FF, Roberts NH et al. (1981) Fault treeFault tree handbook. U.S. Nuclear Regulatory Commission, NUREG-0492, WashingtonGoogle Scholar
  37. 37.
    Wilcox CR, Ayyub MB (2003) Uncertainty modeling of data and uncertainty propagation for risk studies. IEEE Proceedings on Uncertainty Modeling and Analysis, pp 184–191Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Process Engineering, Faculty of Engineering and Applied ScienceMemorial UniversitySt John’sCanada

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