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.     

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.     

Inspection and maintenance planning of pipeline under external corrosion considering generation of new defects

Safe operation of aging pipeline systems under external corrosion can be achieved through inspection and maintenance programs. Tools used for the pipeline inspection are uncertain in detecting a corrosion defect and in sizing a detected defect. The process of generation of new corrosion pits is an uncertain process. These uncertainties must be taken into account in the reliability analysis and in the pipeline inspection and maintenance planning. In this paper the effect of corrosion defect size on the remaining pipeline strength is modeled by a Markov process. Analytical solution of the probability transition matrix is obtained by solving the Kolmogorov forward differential equation. The matrix of probability transition function, the probability of defect detection and the probability distribution of sizing a detected defect is incorporated in estimating the probability of failure. The generation of new corrosion defects is modeled by a Poisson process. The optimal inspection and maintenance schedules are selected based on the reliability constraint. The sensitivity of optimal inspection schedule to the quality of inspection tools and to maintenance criteria is illustrated through examples.     

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.     

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.     

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。     

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.     

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.     

埋地管道腐蚀检测与评价技术

介绍了土壤腐蚀环境的检测、管道防腐层检测技术、管体腐蚀损伤检测技术及杂散电流检测技术,探讨了管道剩余强度评价方法及管道剩余寿命预测方法。     

热力管道状态检测技术研究

分析了国内外管道检测技术的原理、特点与应用领域,以及在热力管道检测中的适用性。在可通行管沟中可采用红外热像仪检测法对热力管道进行局部检测。采用分布式光纤测温系统可对管沟敷设热力管道和直埋敷设热力管道实现全程连续检测。管道内检测技术包括测径器检测法、闭路电视（CCTV）管道检测法、超声波检测法、漏磁检测法,这些方法尽管在石油管道检测中非常有效,但由于热力管道的高温和检测设备进出困难,仍应用较少。管道内检测技术对于管道的快速检测、完整性评价意义重大,值得进一步研究。     

基于深度学习的排水管道缺陷内窥检测智能识别系统研究

目前国内各城市已普遍采用管道机器人深入管道内部摄取视频影像,有效获取到可供管道缺陷检测的一手资料,但缺陷识别大部分依靠人工目视识别,耗时耗力,生产周期长。利用福州市勘测院多年累积的管道检测数据,基于Pytorch深度学习框架、建立了排水管道缺陷内窥检测智能识别系统,包括:数据预处理,残差神经网络设计与训练、系统集成等。重点实现了三级组合识别模型建构(二分类,类型识别,等级识别),解决了系统准确度等技术难题。经生产实践表明:模型准确率高,可有效提高管道健康状况检查质量和效率。     

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.     

Redundancy-based service life assessment of corroded reinforced concrete elements considering parameter uncertainties

In order to assess the structural reliability and redundancy with respect to deterioration, appropriate models have to be selected which adequately describe the deterioration process. The parameters associated with these models have to be estimated based on statistical inference. In general, the uncertainties that arise from the estimation of parameters are not accounted for in reliability assessment and the obtained structural reliability indices are assumed to be constant values. When parameter uncertainties are considered, the structural reliability index can, however, be considered as a random variable which inevitably influences the full-probabilistic decision-making process. Furthermore, the structural reliability indices which are used in a reliability-based redundancy factor can be considered as random variables. Hence, this redundancy factor itself is a random variable as well. In this paper, a full-probabilistic framework is developed which allows for the service life assessment of reinforced concrete elements subjected to corrosion based on a probability-based redundancy factor and taking into account parameter uncertainties. It is proven that these parameter uncertainties have a significant influence on the lifetime estimates of concrete elements subjected to corrosion. Finally, a simplified method is proposed which allows to incorporate parameter uncertainties in the redundancy assessment.     

管道煤气泄漏事故评估的不确定性

讨论管道煤气泄漏事故风险评价过程中泄漏源强度、扩散攻伤害模式的不确定性问题。采用随机抽样推断法对管道煤气泄漏源的输入参数的不确定性总理２进行定量的分析,并且讨论了泄漏时间的不定性与确定泄漏源强度的关系,提出了管道气全泄漏时间估算方法。对于管道煤气泄漏事故可采用定量的比较分析方法说明扩散模式和伤害模式的不定性问题。     

Simulation-based deterioration patterns of water pipelines

Water pipelines deteriorate overtime due to several distressing factors. To keep water pipelines in good condition, municipalities need to use reliable and credible deterioration models and inspection plans to better manage their rehabilitation and maintenance. Thus, this paper presents the development of deterioration models and patterns of water pipelines. The deterioration models consider different water pipe sizes and materials as well as different surrounding environmental conditions which affect their deterioration rates. As a prerequisite to the development of such deterioration models, a condition assessment model for water pipelines was first developed. Questionnaires were distributed among experts to determine the weights of the factors affecting water pipeline conditions using the fuzzy analytic network process. Monte-Carlo simulation was used to account for the large uncertainties of the calculated weights in the development of the condition assessment model. The validation of the model, which was performed using historical data, yielded an average validity percentage of 93.59%. The developed models are expected to help municipalities and decision makers to accurately plan for future water pipelines maintenance and rehabilitation activities based on their different deterioration patterns. It takes into consideration both the uncertainties at the initial stage and those accumulated during the calculation process.     

Life-Cycle Performance of RC Bridges: Probabilistic Approach

This article addresses the problem of reliability assessment of reinforced concrete (RC) bridges during their service life. First, a probabilistic model for assessment of time-dependent reliability of RC bridges is presented, with particular emphasis placed on deterioration of bridges due to corrosion of reinforcing steel. The model takes into account uncertainties associated with materials properties, bridge dimensions, loads, and corrosion initiation and propagation. Time-dependent reliabilities are considered for ultimate and serviceability limit states. Examples illustrate the application of the model. Second, updating of predictive probabilistic models using site-specific data is considered. Bayesian statistical theory that provides a mathematical basis for such updating is outlined briefly, and its implementation for the updating of information about bridge properties using inspection data is described in more detail. An example illustrates the effect of this updating on bridge reliability.     

Risikobasierte Inspektionsstrategien für den optimalen Unterhalt von Tragwerken

Risk based inspection planning for structural systems. Inspections may reduce the uncertainties related to the integrity of structures. These uncertainties, which are particularly large for deterioration processes such as fatigue and corrosion, are discussed in the paper. It is demonstrated how the reduction of uncertainties through inspections can be modeled and quantified. Exemplified by a joint subject to fatigue it is shown how the associated risk reduction, and therefore the benefits of an inspection strategy, can be determined. Finally an overview on some recent applications of the methodology for offshore structures subject to fatigue is presented.     

考虑随机腐蚀作用的埋地管线地震反应分析

利用齐次马尔可夫过程模拟埋地管线腐蚀的发生,并利用线性腐蚀模型模拟埋地管线腐蚀的发展,将线性腐蚀模型中腐蚀速率考虑为确定性参数,推导给出了管线面积腐蚀率随服役时间变化的概率密度函数,并获得了管线截面面积随服役时间变化的均值和方差。在此基础上,根据弹性地基梁模型,将管线周围土体位移正交展开为余弦级数的形式,获得了地震作用下腐蚀管线位移反应和应力反应的解析表达式。对腐蚀管线地震反应进行线性展开,采用随机摄动理论推导给出了腐蚀管线在地震激励下位移和应力反应的均值和标准差。利用上述分析模型对一根200 m长的埋地管线进行了实例分析,结果表明建议模型可以反映管线面积随服役时间的变化规律,并能反映腐蚀管线在地震作用下位移反应和应力反应的基本概率特征。     

基于GIS的排水管道检测数据管理系统设计与实现

排水管道检测数据管理尚处于萌芽状态,没有系统的管理措施,更缺少有效的应用。本文基于管道检测海量视频数据的管理与应用,从城市空间信息管理的角度,设计并实现管道检测数据的管理系统,并将其作为子系统,接入现有城市空间信息管理系统,挖掘检测数据的深层信息,实现综合应用。     

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。