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 combinin...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.展开更多
基金financial support provided by the Natural Sciences and Engineering Research Council(NSERC)of Canada and TransCanada Corporation through the Collaborative Research and Development(CRD)program.
文摘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.
