Reliability-based temporal and spatial maintenance strategy for integrity management of corroded underground pipelines

Abstract In this work, a novel stochastic model framework for predicting the external corrosion growth in buried pipeline structures has been developed, and a reliability-based temporal and spatial maintenance strategy is presented. The spatial correlation of soil properties is modelled via hidden Markov random field. The temporal correlation of the corrosion rate is characterised by the geometric Brownian bridge process. A Bayesian inferential framework is employed to estimate the model parameters of the corrosion growth model using in-line inspection data. The proposed corrosion growth model was validated with actual inspection data. In the reliability analysis, the impact of device detectability is considered and hence the estimated failure probability is more realistic. The proposed maintenance strategy is directly based on the time-specific and location-specific failure probability. The application of the proposed model and maintenance strategy is illustrated through a real-life pipeline system. The results indicate that the proposed maintenance strategy is an adaptive and dynamic scheme that is able to improve the efficiency of inspections.

[1]  Sotirios Chatzis,et al.  A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation , 2008, IEEE Transactions on Fuzzy Systems.

[2]  Alaa Chateauneuf,et al.  Maintenance planning under imperfect inspections of corroded pipelines , 2013 .

[3]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[5]  Wei Wang,et al.  Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector Regression , 2012, Comput. Aided Civ. Infrastructure Eng..

[6]  G. Frankel.  Pitting Corrosion of Metals A Review of the Critical Factors , 1998 .

[7]  Alfred Barbian,et al.  In-Line Inspection of High Pressure Transmission Pipelines: State-of-the-Art and Future Trends , 2012 .

[8]  André T. Beck,et al.  Optimal inspection and design of onshore pipelines under external corrosion process , 2014 .

[9]  Hao Qin,et al.  Inverse Gaussian process-based corrosion growth model for energy pipelines considering the sizing error in inspection data , 2013 .

[10]  Daniel Straub,et al.  Risk based inspection planning for structural systems , 2005 .

[11]  H. Saunders,et al.  Probabilistic models of cumulative damage , 1985 .

[12]  D. M. Titterington,et al.  Multidimensional Markov Chain Models for Image Textures , 1991 .

[13]  Francisco Caleyo,et al.  Criteria for performance assessment and calibration of in-line inspections of oil and gas pipelines , 2007 .

[14]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[15]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[16]  F. Caleyo,et al.  Predictive Model for Pitting Corrosion in Buried Oil and Gas Pipelines , 2009 .

[17]  Wenxing Zhou,et al.  System reliability of corroding pipelines , 2010 .

[18]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[19]  Wenxing Zhou,et al.  Probabilistic characterisation of metal-loss corrosion growth on underground pipelines based on geometric Brownian motion process , 2015 .

[20]  John Dalsgaard Sørensen,et al.  Reliability-Based Optimization in Structural Engineering , 1994 .

[21]  Han Ping Hong,et al.  RELIABILITY BASED OPTIMAL INSPECTION AND MAINTENANCE FOR PIPELINE UNDER CORROSION , 1997 .

[22]  Hui Wang,et al.  A clustering based method to evaluate soil corrosivity for pipeline external integrity management , 2015 .

[23]  Han Ping Hong,et al.  Inspection and maintenance planning of pipeline under external corrosion considering generation of new defects , 1999 .

[24]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[25]  Robert Y. Liang,et al.  The Application of Macro Modeling Concept for the Soil/Coating External Corrosion for ECDA Process by Using Statistical Tools , 2014 .

[26]  Felipe Alexander Vargas Bazán,et al.  Stochastic process corrosion growth models for pipeline reliability , 2013 .

[27]  Jian Zhang,et al.  Advanced Markov Chain Monte Carlo Approach for Finite Element Calibration under Uncertainty , 2013, Comput. Aided Civ. Infrastructure Eng..

[28]  Norman C. Beaulieu,et al.  An optimal lognormal approximation to lognormal sum distributions , 2004, IEEE Transactions on Vehicular Technology.

[29]  Robert Y. Liang,et al.  A clustering approach for assessing external corrosion in a buried pipeline based on hidden Markov random field model , 2015 .

[30]  P. Deb.  Finite Mixture Models , 2008 .

[31]  Hossam A. Gabbar,et al.  Review of pipeline integrity management practices , 2010 .

[32]  Donavan Marney,et al.  The science of pipe corrosion: A review of the literature on the corrosion of ferrous metals in soils , 2012 .

[33]  F. Caleyo,et al.  Markov chain modelling of pitting corrosion in underground pipelines , 2009 .

[34]  H. Haario,et al.  An adaptive Metropolis algorithm , 2001 .

[35]  E. S. Rodriguez,et al.  Part II: Development of a General Failure Control System for Estimating the Reliability of Deteriorating Structures , 1989 .

[36]  Juan J. Salinas,et al.  Risk assessment methodology for pipeline systems , 1985 .

[37]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[38]  Wenxing Zhou,et al.  Bayesian Model for Calibration of ILI Tools , 2012 .

[39]  Y. Katano,et al.  Predictive model for pit growth on underground pipes , 2003 .

[40]  Delmo Santiago Vaitsman,et al.  Evaluation of the corrosivity of the soil through its chemical composition. , 2007, The Science of the total environment.

[41]  Hui Wang,et al.  Bayesian Modeling of External Corrosion in Underground Pipelines Based on the Integration of Markov Chain Monte Carlo Techniques and Clustered Inspection Data , 2015, Comput. Aided Civ. Infrastructure Eng..

[42]  William A. Janos,et al.  Tail of the distribution of sums of log-normal variates , 1970, IEEE Trans. Inf. Theory.

[43]  J. Alamilla,et al.  Modelling steel corrosion damage in soil environment , 2009 .

[44]  F. Caleyo,et al.  Probability distribution of pitting corrosion depth and rate in underground pipelines: A Monte Carlo study , 2009 .

[45]  D. Macdonald,et al.  Unification of the deterministic and statistical approaches for predicting localized corrosion damage. I. Theoretical foundation , 2004 .

[46]  Palle Thoft-Christensen,et al.  Optimal strategy for inspection and repair of structural systems , 1987 .