This paper describes an inverse Gaussian process-based model to characterize the growth of metal-loss corrosion defects on energy pipelines. The model parameters are evaluated using the Bayesian methodology by combining the inspection data obtained from multiple inspections with the prior distributions. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to numerically evaluate the posterior marginal distribution of each individual parameter. The measurement errors associated with the ILI tools are considered in the Bayesian inference. The application of the growth model is illustrated using an example involving real inspection data collected from an in-service pipeline in Alberta, Canada. The results indicate that the model in general can predict the growth of corrosion defects reasonably well. Parametric analyses associated with the growth model as well as reliability assessment of the pipeline based on the growth model are also included in the example. The proposed model can be used to facilitate the development and application of reliability-based pipeline corrosion management.
. Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines[J]. Frontiers of Structural and Civil Engineering, 2013, 7(3): 276-287.
Hao QIN, Shenwei ZHANG, Wenxing ZHOU. Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines. Front Struc Civil Eng, 2013, 7(3): 276-287.
Hong H P. Application of stochastic process to pitting corrosion. Corrosion , 1999, 55(1): 10–16 doi: 10.5006/1.3283958
2
Maes M A, Faber M H, Dann M R. Hierarchical modeling of pipeline defect growth subject to ILI uncertainty. In: Proceedings of the ASME 28th International Conference on Ocean . Offshore and Arctic Engineering, Honolulu, Hawaii, USA, 2009, OMAE2009–79470
3
Zhang S, Zhou W, Al-Amin M, Kariyawasam S, Wang H. Time-dependent corrosion growth modeling using multiple ILI data. In: Proceedings of IPC 2012, IPC2012-90502. ASME, Calgary , 2012
4
Wang X, Xu D. An inverse Gaussian process model for degradation data. Technometrics , 2010, 52(2): 188–197 doi: 10.1198/TECH.2009.08197
5
Chikkara R S, Folks J L. The Inverse Gaussian Distribution, New York: Marcell Dekker, 1989
6
Al-Amin M, Zhou W, Zhang S, Kariyawasam S, Wang H. Bayesian model for the calibration of ILI tools, In: Proceedings of IPC 2012, IPC2012-90491 . ASME, Calgary, 2012
7
Fuller W A. Measurement Error Models. New York: John Wiley & Sons, Inc, 1987
8
Jaech J L. Statistical Analysis of Measurement Errors. New York: John Wiley & Sons, Inc, 1985
9
Bernardo J, Smith A F M. Bayesian Theory. New York: John Wiley & Sons Inc, 2007
10
Gelman A, Carlin J B, Stern H S, Rubin D B. Bayesian Data Analysis, 2nd edition, New York: Chapman & Hall/CRC, 2004
11
Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: Evolution, critique and future directions. Statistics in Medicine , 2009, 28(25): 3049–3082 doi: 10.1002/sim.3680
12
CSA-Z662 Oil and Gas Pipeline Systems. Canadian Standards Association, Rexdale, Ontario, 2007
13
Zhou W, Hong H P, Zhang S. Impact of dependent stochastic defect growth on system reliability of corroding pipelines. International Journal of Pressure Vessels and Piping , 2012, 96-97: 68–77 doi: 10.1016/j.ijpvp.2012.06.005
14
Zhou W. Reliability evaluation of corroding pipelines considering multiple failure modes and time-dependent internal pressure. Journal of Infrastructure Systems , 2011, 17: 216–224
15
Leis B N, Stephens D R. An alternative approach to assess the integrity of corroded line pipe- part II: alternative criterion. In: Proceedings of the 7th International Offshore and Polar Engineering Conference . Honolulu, 1997, 635–640
16
Kiefner J F, Maxey W A, Eiber R J, Duffy A R. Failure stress levels of flaws in pressurized cylinders, Progress in flaw growth and fracture toughness testing, ASTM STP 536. American Society of Testing and Materials ; 1973, 461–481
17
Zhou W. System reliability of corroding pipelines. International Journal of Pressure Vessels and Piping , 2010, 87(10): 587–595 doi: 10.1016/j.ijpvp.2010.07.011
Zhou W, Huang G. Model error assessments of burst capacity models for corroded pipelines. International Journal of Pressure Vessels and Piping , 2012, 99-100: 1–8 doi: 10.1016/j.ijpvp.2012.06.001