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Research Papers: Operations, Applications and Components

Risk-Based Maintenance Planning for Deteriorating Pressure Vessels With Multiple Defects

[+] Author and Article Information
Shane Haladuick

Department of Civil Engineering,
University of Calgary,
2500 University Drive NW,
Calgary, AB T2N 1N4, Canada
e-mail: smhaladu@ucalgary.ca

Markus R. Dann

Department of Civil Engineering,
University of Calgary,
2500 University Drive NW,
Calgary, AB T2N 1N4, Canada
e-mail: mrdann@ucalgary.ca

1Corresponding author.

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received December 8, 2016; final manuscript received March 29, 2017; published online April 21, 2017. Assoc. Editor: Steve J. Hensel.

J. Pressure Vessel Technol 139(4), 041602 (Apr 21, 2017) (8 pages) Paper No: PVT-16-1233; doi: 10.1115/1.4036428 History: Received December 08, 2016; Revised March 29, 2017

Pressure vessels are subject to deterioration processes, such as corrosion and fatigue, which can lead to failure. Inspections and repairs are performed to mitigate this risk. Large industrial facilities (e.g., oil and gas refineries) often have regularly scheduled shutdown periods during which many components, including the pressure vessels, are disassembled, inspected, and repaired if necessary. This paper presents a decision analysis framework for the risk-based maintenance (RBM) planning of corroding pressure vessels. After a vessel has been inspected, this framework determines the optimal maintenance time of each defect, where the optimal time is the one that minimizes the total expected cost over the lifecycle of the vessel. The framework allows for multiple defects and two failure modes (leak and burst), and accounts for the dependent failure events. A stochastic gamma process is used to model the future deterioration growth to determine the probability of vessel failure. The novel growth model presents a simple method to predict both the depth and length of each corrosion defect to enable burst analysis. The decision analysis framework can aid decision makers in deciding when a repair or replacement should be performed. This method can be used to immediately inform the decision maker of the optimal decision postinspection. A numerical example of a corroding pressure vessel illustrates the method.

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References

Luce, R. D. , and Raiffa, H. , 1957, Games and Decisions, Wiley, New York.
Kahn, F. I. , and Haddara, M. M. , 2003, “ Risk-Based Maintenance (RBM): A Quantitative Approach for Maintenance/Inspection Scheduling and Planning,” J. Loss Prev. Process Ind., 16(6), pp. 561–573. [CrossRef]
Von Neumann, J. , and Morgenstern, O. , 1947, Theory of Games and Economical Behaviour, 2nd ed., Princeton University Press, Princeton, NJ.
Pratt, J. W. , Raiffa, H. , and Schlaifer, R. , 1995, Introduction to Statistical Decision Theory, MIT Press, Cambridge, MA.
Jordaan, I. , 2005, Decisions Under Uncertainty; Probabilistic Analysis for Engineering Decisions, Cambridge University Press, Cambridge, UK.
JCSS, 2008, “ Risk Assessment in Engineering; Principles, System Representation & Risk Criteria,” The Joint Committee on Structural Safety, Technical University of Denmark, Lyngby, Denmark.
Parmigiani, G. , and Inoue, L. , 2009, Decision Theory, Principles and Approaches, Wiley, Chichester, UK.
Xu, Z. , 2015, Uncertain Multi-Attribute Decision Making: Methods and Applications, Springer, Berlin.
ASME, 2007, “ Inspection Planning Using Risk-Based Methods,” American Society of Mechanical Engineers, New York, Standard No. PCC-3-2007.
API, 2009, “ Recommended Practice for Risk-Based Inspection,” American Petroleum Institute, Washington, DC, Standard No. API 580-2009.
API, 2016, “ Recommended Practice for Risk-Based Inspection Technology,” American Petroleum Institute, Washington, DC, Standard No. API 581-2016.
Hellevik, S. G. , Langen, I. , and Sørensen, J. D. , 1999, “ Cost Optimal Reliability Based Inspection and Replacement Planning of Piping Subjected to CO2 Corrosion,” Int. J. Pressure Vessels Piping, 76(8), pp. 527–538. [CrossRef]
Goyet, J. , Straub, D. , and Faber, M. H. , 2002, “ Risk-Based Inspection Planning of Offshore Installations,” Struct. Eng. Int., 12(3), pp. 200–208. [CrossRef]
Straub, D. , 2004, “ Generic Approaches to Risk Based Inspection Planning for Steel Structures,” Ph.D. dissertation, Swiss Federal Institute of Technology Zürich, Zürich, Switzerland.
Gross, R. E. , Mitchell, E. M. , and Harris, S. P. , 2012, “ Evaluation of Maintenance Intervals for Spring-Operated Relief Valves Using a Risk-Based Inspection Technique,” ASME J. Pressure Vessel Technol., 134(6), p. 061601. [CrossRef]
Sahraoui, Y. , Khelif, R. , and Chateauneuf, A. , 2013, “ Maintenance Planning Under Imperfect Inspections of Corroded Pipelines,” Int. J. Pressure Vessels Piping, 104, pp. 76–82. [CrossRef]
Gomes, W. J. S. , Beck, A. T. , and Haukaas, T. , 2013, “ Optimal Inspection Planning for Onshore Pipelines Subject to External Corrosion,” Reliab. Eng. Syst. Safety, 118, pp. 18–27. [CrossRef]
Gomes, W. J. S. , and Beck, A. T. , 2014, “ Optimal Inspection and Design of Onshore Pipelines Under External Corrosion Process,” Struct. Saf., 47, pp. 48–58. [CrossRef]
Hong, H. P. , 1997, “ Reliability Based Optimal Inspection and Maintenance for Pipeline Under Corrosion,” Civ. Eng. Syst., 14(4), pp. 313–334. [CrossRef]
Hong, H. P. , 1999, “ Inspection and Maintenance Planning of Pipeline Under External Corrosion Considering Generation of New Defects,” Struct. Saf., 21(3), pp. 203–222. [CrossRef]
Pandey, M. D. , 1998, “ Probabilistic Models for Condition Assessment of Oil and Gas Pipelines,” NDT&E Int., 31(5), pp. 349–358. [CrossRef]
Garbatov, Y. , and Soares, C. G. , 2001, “ Cost and Reliability Based Strategies for Fatigue Maintenance Planning of Floating Structures,” Reliab. Eng. Syst. Saf., 73(3), pp. 293–301. [CrossRef]
Nessim, M. A. , Stephens, M. J. , and Zimmerman, T. J. E. , 2000, “ Risk-Based Maintenance Planning for Offshore Pipelines,” Offshore Technology Conference (OTC), Houston, TX, May 1–4, SPE Paper No. OTC-12169-MS.
Zhang, S. , and Zhou, W. , 2014, “ Cost-Based Optimal Maintenance Decisions for Corroding Natural Gas Pipelines Based on Stochastic Degradation Models,” Eng. Struct., 74, pp. 74–85. [CrossRef]
Chang, M. K. , Chang, R. R. , Shu, C. M. , and Lin, K. N. , 2005, “ Application of Risk Based Inspection in Refinery and Processing Piping,” J. Loss Prev. Process Ind., 18(4–6), pp. 397–402. [CrossRef]
API, 2007, “ Fitness for Service,” American Petroleum Institute, Washington, DC, Standard No. API 579-1-2007.
Rackwitz, R. , Lentz, A. , and Faber, M. , 2005, “ Socio-Economically Sustainable Civil Engineering Infrastructures by Optimization,” Struct. Saf., 27(3), pp. 187–229. [CrossRef]
Holland, J. H. , 1976, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
Goldberg, D. E. , 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Longman Publishing, Boston, MA.
Pandey, M. D. , Yuan, X. X. , and van Noortwijk, J. M. , 2009, “ The Influence of Temporal Uncertainty of Deterioration on Life-Cycle Management of Structures,” Struct. Infrastruct. Eng., 5(2), pp. 145–156. [CrossRef]
Brazán, F. A. V. , and Beck, A. T. , 2013, “ Stochastic Process Corrosion Growth Models for Pipeline Reliability,” Corros. Sci., 74, pp. 50–58. [CrossRef]
Maes, M. A. , 2007, “ Exchangeable Condition States and Bayesian Reliability Updating,” 13th IFIP WG7.5 Working Conference on Reliability and Optimization of Structural Systems, Kobe, Japan, Oct. 11–14, pp. 27–42.
Pandey, M. D. , and Van Noortwijk, J. M. , 2004, “ Gamma Process Model for Time-Dependent Structural Reliability Analysis,” Second International Conference on Bridge Maintenance, Safety, and Management (IABMAS), Kyoto, Japan, Oct. 18–22.
van Noortwijk, J. M. , 2009, “ A Survey of the Application of Gamma Processes in Maintenance,” Reliab. Eng. Syst. Saf., 94(1), pp. 2–21. [CrossRef]
Koller, D. , and Friedman, N. , 2009, Probabilistic Graphical Models: Principles and Techniques, MIT Press, Cambridge, UK.
Gamerman, D. , and Lopes, H. F. , 2006, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd ed., CRC Press, Boca Raton, FL.
Spiegelhalter, D. , Thomas, A. , Best, N. , and Lunn, D. , 2006, “ OpenBUGS User Manual, Version 2.20,” MRC Biostatistics Unit, Cambridge, UK.
Kaida, T. , Izumi, S. , and Sakai, S. , 2013, “ Sensitivity Analysis of Fitness-for-Service Assessment Based on Reliability for Cylindrical Pressure Vessels With Local Metal Loss,” ASME J. Pressure Vessel Technol., 135(6), p. 061202. [CrossRef]
Melchers, R. E. , 1999, Structural Reliability Analysis and Prediction, 2nd ed., Wiley, Chichester, UK.
Leira, B. J. , Naess, A. , and Naess, O. E. , 2014, “ Reliability of Corroded Pipelines by Enhanced Monte Carlo Simulation,” ASME Paper No. OMAE2014-24583.
POF, 2016, “ Specifications and Requirements for Intelligent Pig Inspection of Pipelines,” Pipeline Operators Forum, The Netherlands.

Figures

Grahic Jump Location
Fig. 1

Hierarchical graphical model of the corrosion growth process

Grahic Jump Location
Fig. 2

Probability of failure using three analysis methods

Grahic Jump Location
Fig. 3

Expected lifecycle cost comparison of the three analysis methods to assess the impact of system reliability

Grahic Jump Location
Fig. 4

Expected lifecycle cost of the top four ranked repair plans for the system reliability method; NR is “no repair”

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