RELIABILITY BASED OPTIMAL INSPECTION AND MAINTENANCE FOR PIPELINE UNDER CORROSION

Nondestructive inspection tools used for pipeline inspection are uncertain in detecting corrosion pits and in sizing detected defects. Probability-based optimal inspection schedule analysis must taken these uncertainties into account. In this paper, the probability of time to failure is formulated as integral equations with domain of integration expressed as unions and intersections of domains of failure, defect detection, defect nondetection and maintenance criterion. The rate of defect detection as a function of defect size and the maintenance criterion are used as filters to eliminate the defects that are not fit for service in an expected remaining service life after inspection. Simulation procedure is given to estimate the probability distribution of time to failure by using the integral equations. To facilitate the probabilistic analysis, a standard uniformly distributed variate is introduce and used in defining the domain of detected defect and the domain of nondetected defect. The advantages of using the proposed simulation procedure are discussed. Optimal inspection schedules are selected based on the minimum value of the maximum probability of time to failure before inspections and before the time at the end of service life. Effect of inspection quality and maintenance criterion on probability of time to failure and on selecting optimal inspection schedule is presented through an illustrative application study.     

Reliability analysis with nondestructive inspection

Nondestructive inspection tools are commonly used to inspect structures or structural components with resistance deterioration due to defect size growth. The quality of the tools is mainly defined by the rate of detecting a defect with defect size s, (s), and the accuracy in sizing a detected defect. The uncertainty of sizing a detected defect can be incorporated in limit state functions that include defect size, and a reliability evaluation can be carried out with the efficient first-order reliability method (FORM). The rate of detecting a defect can also be incorporated in the reliability evaluation of an inspected structure or structural component. This is done, in this paper, by introducing a standard normally distributed variate, Z, and defining a limit state function as a function of and (s). Advantages of using this limit state function, rather than using a limit state function based on the actual defect size and the critical defect size distributed according to the rate of detection curve, are discussed. It is shown that one only needs to use the mean rate of defect detection curve to consider the uncertainty in the rate of detection. The incorporation of the uncertainty in rate of detection for reliability updating analysis with inspection results, and for reliability-based selection of optimal inspection and maintenance schedule for resistance deteriorating structures are also presented. The proposed approach is illustrated by two examples in evaluating reliability with inspection information and in selecting an optimal inspection and maintenance schedule by minimizing the probability of time to failure before inspection and before the time at the end of remaining service life.     

Inspection and maintenance planning of underground pipelines under the combined effect of active corrosion and residual stress

In this paper, a maintenance policy is proposed for pipelines subjected to active corrosion and residual stress, by taking into account imperfect inspection results. The degradation of the pipeline is induced by uniform corrosion, leading to losses of the pipe wall thickness. Localized corrosion is not considered herein, as neither pitting nor crevice corrosion are strongly influenced by external loading conditions and, hence, are not critical in structural strength considerations. When the corroded layers are removed, strain relaxation occurs, causing a redistribution of residual stresses. In parallel, the inspection is applied to detect the corrosion defects, namely the thickness of the corroded layer, and it has a detection threshold under which no corrosion rate can be measured. Due to uncertainties, each inspection is affected by the probability of detecting small defects and the probability of wrong assessment in terms of defect existence and size. The present work aims at integrating imperfect inspection results in the cost model for corroded pipelines, where the failure probabilities are computed by reliability methods. A numerical application on a gas pipe shows the influence of corrosion rates and residual stresses on the optimal maintenance planning.     

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

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.     

基于成本的长输管道腐蚀缺陷检测及维修规划

提出了基于成本的长输管道腐蚀缺陷的检测及维修模型,将总成本分为检测和维修2部分,根据总成本最小原则确定最优检测及维修规划。最优的检测及维修,是在保证管道在设计工作寿命内的最大腐蚀深度小于满足强度要求的最大腐蚀裕量的基础上,使管道生命周期内总的期望费用最小。针对腐蚀缺陷的发展过程,以最早产生缺陷的尺寸为界限进行划分,对不同腐蚀程度缺陷进行分类修复。通过比较在不同的状态点下进行检测修复所产生的检测和维修总费用,确定最优的检测及维修规划。     

城市燃气管道应力腐蚀分析

随着城市燃气管道运营年代的推移,管道的寿命也面临着挑战,其中应力腐蚀是造成管道事故的主要原因。文章给出了城市燃气管道应力腐蚀模型,提出可以根据应力腐蚀过程采取有效措施,对燃气管道预先检测和维护,避免发生管道事故。     

在役抗震结构基于马尔科夫过程的最优维修策略分析

根据抗震设防的三水准要求 ,对结构可能存在的状态分类并设定相应的维修策略。采用马尔科夫过程理论 ,建立了在役抗震结构最优维修策略的数学模型 ,分别确定了在不同维修策略下的状态转移概率矩阵和报酬矩阵 ,并计算结构的极限状态转移概率向量和期望总花费。通过对维修策略的优化分析 ,得出了期望总花费最小者为最优维修策略。这样可以根据结构所处的不同状态安排相应的最优维修策略 ,从而为决策提供了科学的依据     

Corrosion initiation time updating by epistemic uncertainty as an alternative to schedule the first inspection time of pre-stressed concrete vehicular bridge beams

As traffic demands grow constantly and some vehicle bridges deteriorate because of corrosion issues, bridge agencies require non-expensive procedures to support decisions about cost-effective maintenance schedules. In this article, a reliability-based formulation is proposed for the prediction of the optimal first inspection time including both the corrosion deterioration and the epistemic uncertainty on the corrosion initiation time. For the identification of the bridge integrity state, where little or no follow-up has been previously developed, the prediction of a damage state implies a great deal of epistemic uncertainty. The impact of this kind of uncertainty on the corrosion initiation time prediction is appraised in order to include the conservative estimations of such a time, according to the bridge revenues/cost ratio of further and more detailed studies. The time-varying bridge reliability is calculated in terms of the bridge corrosion deterioration, which induces a moment capacity reduction of the bridge beams. Epistemic uncertainty is introduced in the corrosion initiation time, and the optimal first inspection time is obtained as a probability distribution. Consequently, a procedure to calculate the first time for inspection on girder bridges has been proposed, based on updating a known distribution after considering the effect of epistemic uncertainty, using a lognormal distributed factor as ‘evidence’, by means of the Markov chain Monte Carlo technique.     

Evaluating the system reliability of corroding pipelines based on inspection data

This paper presents a methodology for evaluating the time-dependent system reliability of a pressurised gas pipeline segment containing multiple active metal-loss corrosion defects. The methodology incorporates three distinctive failure modes of the pipe segment due to corrosion, namely small leak, large leak and rupture. The growth of the depth of individual corrosion defect is assumed to follow a power-law function of time. The Bayesian updating and Markov Chain Monte Carlo (MCMC) simulation techniques are used to quantify the parameters of the power-law growth model based on data obtained from multiple inspections carried out at different times. The simple Monte Carlo and MCMC techniques are combined to evaluate the system reliability. A numerical example involving an in-service gas pipeline located in Alberta, Canada, is used to illustrate the proposed methodology. Results of the sensitivity analysis suggest that the use of a defect-specific or segment-specific growth model for the defect depth has a marked impact on the evaluated system reliability. The proposed methodology can be incorporated in reliability-based pipeline corrosion management programmes to assist integrity engineers in making informed decisions about defect repair and mitigation.     

基于马尔可夫链模型燃气管道维修方案的确定

分析了埋地燃气管道失效机理,介绍了马尔可夫链模型及埋地管道系统的转移概率矩阵。以埋地钢质管道为例,阐述了埋地管道系统中马尔可夫决策过程的求解与应用,为埋地管道维修方案的确定提供了依据。     

Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines

A fuzzy artificial neural network (ANN)–based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems.     

Multi-objective maintenance strategy for in-service corroding pipelines using genetic algorithms

Abstract

The multi-objective optimisation technique utilising genetic algorithms is employed to develop the optimal maintenance strategy for corroding oil and gas pipelines. The objective functions of the optimisation are the maximum annual conditional probabilities of small leak and burst, respectively, of all the pipe joints included in the pipeline segment over a predefined time horizon, and the total present-value cost of corrosion repair. The allowable annual probabilities of small leak and burst, and the annual repair budget are treated as constraints in the optimisation. The proposed optimal maintenance strategy is illustrated using a natural gas pipeline segment consisting of 90 corroding pipe joints. The analysis results indicate that a diverse set of solutions are included in the obtained Pareto front, which allow the decision-maker to select the maintenance plan achieving the desired tradeoff between the reliability and cost. The approach presented in this study can be incorporated in the practical optimal maintenance planning of corroding pipelines subjected to safety and resource constraints.     

基于可靠度的腐蚀混凝土排水管道评估方法

介绍了可靠度的基本概念,对可靠度用于排水管道腐蚀评估的意义进行了分析,并将结构设计普遍应用的可靠度理论应用于腐蚀钢筋混凝土排水管道的评估实践中,同时给出了实际计算方法,达到了优化钢筋混凝土排水管道修复设计的目的。     

Bayesian Modeling of External Corrosion in Underground Pipelines Based on the Integration of Markov Chain Monte Carlo Techniques and Clustered Inspection Data

In this study, a model is developed to assess external corrosion in buried pipelines based on the unification of Bayesian inferential structure derived from Markov chain Monte Carlo techniques using clustered inspection data. This proposed stochastic model combines clustering algorithms that can ascertain the similarity of corrosion defects and Monte Carlo simulation that can give an accurate probability density function estimation of the corrosion rate. The metal loss rate is chosen as the indicator of corrosion damage propagation, obeying a generalized extreme value (GEV) distribution. Bayesian theory was employed to update the probability distribution of metal loss rate as well as the GEV parameters in order to account for the model uncertainty. The proposed model was validated with direct and indirect inspection data extracted from a 110‐km buried pipeline system.     

Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis for energy pipelines

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.     

埋地燃气钢管腐蚀检测及防腐对策

阐述了埋地燃气钢管防腐的重要意义,通过对某管道燃气公司埋地燃气钢管腐蚀检测,分析了产生腐蚀的原因,提出了减缓腐蚀的方法与对策,对该公司的外加电流深井阳极阴极保护法的防腐实践进行总结与探讨.     

Improved Bayesian network configurations for probabilistic identification of degradation mechanisms: application to chloride ingress

Probabilistic modelling of deterioration processes is an important task to plan and quantify maintenance operations of structures. Relevant material and environmental model parameters could be determined from inspection data; but in practice, the number of measures required for uncertainty quantification is conditioned by time-consuming and expensive tests. The main objective of this study was to propose a method based on Bayesian networks for improving the identification of uncertainties related to material and environmental parameters of deterioration models when there is limited available information. The outputs of the study are inspection configurations (in space and time) that could provide an optimal balance between accuracy and cost. The proposed methodology was applied to the identification of random variables for a chloride ingress model. It was found that there is an optimal discretisation for identifying each model parameter and that the combination of these configurations minimises identification errors. An illustration to the assessment of the probability of corrosion initiation showed that the approach is useful even if inspection data are limited.     

里地燃气钢管腐蚀检测及防腐对策

阐述了埋地燃气钢管防腐的重要意义,通过对某管道燃气公司埋地燃气钢管腐蚀检测,分析了产生腐蚀的原因,提出了减缓腐蚀的方法与对策,对该公司的外加电流深井阳极阴极保护法的防腐实践进行总结与探讨。     

Value of information-based decision analysis of the optimal next inspection type for deteriorating structural systems

Deteriorating infrastructure systems require inspections and maintenance to ensure safe operation. System operators are often required to decide the optimal type of inspection to perform, where some inspections are of higher accuracy, and correspondingly higher cost. Life cycle analysis is typically used to determine the optimal inspection type. While life cycle analysis is effective at determining the optimal inspection type, it is also inefficient, requiring analysis of the entire decision sequence throughout the system life cycle. This paper presents an efficient methodology to approximate the decision of the optimal next inspection type without performing a life cycle analysis. This methodology determines the range of the value of information provided by only the next inspection. When the inspection cost is outside the range of the value of information then this method yields the decision of which inspection type to choose, negating the need for life cycle analysis. When the inspection cost for some inspection types lies within the bounds then a subsequent life cycle analysis is required, but perhaps some inspection types can be eliminated, simplifying the life cycle analysis. Thus, this method is complimentary to life cycle analysis, functioning as a quick preliminary assessment. The methodology is demonstrated through a numerical example of a corroding pipeline.