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Recurrent pipeline failures continue to be a source of safety and economic risk related to processing, transporting, and distributing natural gas. Studies have shown the lack of comprehensive, integrated, and accessible risk-informed integrity management models and tools for pipeline operators is a major contributor. To address this gap, this research presents a system-level Prognosis and Health Monitoring (PHM) modeling framework for gas pipeline system integrity management to prevent or reduce the likelihood of failures. The proposed PHM approach takes into consideration all possible failure modes of the pipeline under study. It leverages the advancement of sensor technology to stream field data in real-time to perform a dynamic system-level failure analysis based on Hybrid Causal Logic (HCL) including a Dynamic Bayesian Network (DBN) corrosion model, to provide cost-effective and optimal mitigation actions such as sensor placement and maintenance schedule optimizations. The developed models are implemented in a software platform where the pipeline operators can observe the real-time and projected health state of the pipeline and the set of suggested actions to enhance the structural integrity of the pipeline system. The platform includes three main modules: Real-Time Health Monitoring, System-Level Reliability, and Optimal Mitigation Actions. From a safety perspective, the proposed PHM can prevent pipeline failures or reduces their likelihood by supporting pipeline operators in optimal decision-making and planning activities. To demonstrate potential benefits and performance of the proposed framework and software implementation, it is applied in a case study involving a corroding gas transmission pipeline.
Pipeline integrity management refers to an approach of understanding and operating pipelines in a safe and reliable manner. In this work, firstly, a probabilistic predictive model for internal corrosion of natural gas pipelines subject to aqueous CO2/H2S environment has been proposed. The model regards uniform and pitting corrosion as two main corrosion mechanisms and has been calibrated with the experimental data in a deterministic framework. Methodologies of simulating and accounting for temporal and spatial variabilities of operating parameters have been proposed and applied to the model for field applications. The model has been validated against field data from eight wet gas gathering pipelines in a probabilistic framework. Secondly, a reinforcement learning (RL)-based maintenance scheduler has been proposed for pipeline maintenance optimization problems by leveraging the proposed predictive corrosion model and the Q-learning and Sarsa algorithms. A case study has shown the superiority of the proposed maintenance scheduler over the periodic maintenance policy in reducing the maintenance costs. Finally, the previous two parts of work have been integrated into a pipeline system integrity management software featuring pipeline health monitoring, corrosion prognosis, system-level failure analysis, sensor placement optimization, and inspection/maintenance optimization. A case study has been provided to demonstrate the capabilities of the software.
A comprehensive guide to the application and processing of condition-based data to produce prognostic estimates of functional health and life. Prognostics and Health Management provides an authoritative guide for an understanding of the rationale and methodologies of a practical approach for improving system reliability using conditioned-based data (CBD) to the monitoring and management of health of systems. This proven approach uses electronic signatures extracted from conditioned-based electrical signals, including those representing physical components, and employs processing methods that include data fusion and transformation, domain transformation, and normalization, canonicalization and signal-level translation to support the determination of predictive diagnostics and prognostics. Written by noted experts in the field, Prognostics and Health Management clearly describes how to extract signatures from conditioned-based data using conditioning methods such as data fusion and transformation, domain transformation, data type transformation and indirect and differential comparison. This important resource: Integrates data collecting, mathematical modelling and reliability prediction in one volume Contains numerical examples and problems with solutions that help with an understanding of the algorithmic elements and processes Presents information from a panel of experts on the topic Follows prognostics based on statistical modelling, reliability modelling and usage modelling methods Written for system engineers working in critical process industries and automotive and aerospace designers, Prognostics and Health Management offers a guide to the application of condition-based data to produce signatures for input to predictive algorithms to produce prognostic estimates of functional health and life.
Industrial Prognostics predicts an industrial system’s lifespan using probability measurements to determine the way a machine operates. Prognostics are essential in determining being able to predict and stop failures before they occur. Therefore the development of dependable prognostic procedures for engineering systems is important to increase the system’s performance and reliability. Diagnostics and Prognostics of Engineering Systems: Methods and Techniques provides widespread coverage and discussions on the methods and techniques of diagnosis and prognosis systems. Including practical examples to display the method’s effectiveness in real-world applications as well as the latest trends and research, this reference source aims to introduce fundamental theory and practice for system diagnosis and prognosis.
Based on over 40 years of experience in the field, Ramesh Singh goes beyond corrosion control, providing techniques for addressing present and future integrity issues. Pipeline Integrity Handbook provides pipeline engineers with the tools to evaluate and inspect pipelines, safeguard the life cycle of their pipeline asset and ensure that they are optimizing delivery and capability. Presented in easy-to-use, step-by-step order, Pipeline Integrity Handbook is a quick reference for day-to-day use in identifying key pipeline degradation mechanisms and threats to pipeline integrity. The book begins with an overview of pipeline risk management and engineering assessment, including data collection and regulatory approaches to liquid pipeline risk management. Other critical integrity issues include: Pipeline defects and corrective actions Introduction to various essential pipeline material such as line pipes and valves Coverage on corrosion and corrosion protection Identifies the key pipeline degradation mechanisms and threats to pipeline integrity Appreciates various corrosion monitoring and control tools and techniques Understands the principles of risk assessment and be able to conduct a simple risk assessment Develops simple Pipeline Integrity Management plans Selects and apply appropriate inspection and assessment criteria for pipeline defects Recommends appropriate repair methods for pipeline defects
Gas Pipeline Safety: Preliminary Observations on the Implementation of the Integrity Management Program
Mitigation of Gas Pipeline Integrity Problems presents the methodology to enable engineers, experienced or not, to alleviate pipeline integrity problems during operation. It explains the principal considerations and establishes a common approach in tackling technical challenges that may arise during gas production. Covers third-party damage, corrosion, geotechnical hazards, stress corrosion cracking, off-spec sales gas, improper design or material selection, as-built flaws, improper operations, and leak and break detection Details various hazard mitigation options Offers tested concepts of pipeline integrity blended with recent research results, documented in a scholarly fashion to make it simple to the average reader This practical work serves the needs of advanced students, researchers, and professionals working in pipeline engineering and petrochemical industries.
Monitoring the health condition of machinery has been an area of research for quite some time. Despites several advancements, the application of conventional signal analysis and pattern recognition methods face several challenges when the operating variables such as load, speed, and temperature vary considerably for the monitored asset. The residual clustering approach addresses the multi-regime monitoring challenge by first modeling the baseline non-linear correlation relationship in the measured signal features and by providing predicted signal features. Calculating the residual signal features allows one to normalize the effect of the operating variables, since one is considering how the response of the system compares with the predicted response based on the baseline behavior. In many instances the degradation signature of a component or system is more pronounced under certain operating conditions. The clustering portion of the residual clustering method specifically addresses the regime dependent signature aspect and bases the health value on the monitoring regime in which the degradation signature is more prevalent. This dissertation work highlights the mathematical framework and provides guidance on the appropriate processing methods for each portion of the approach. From simulation studies and wind speed data, the results highlight that the auto-associative neural network method provides the lowest prediction error when compared with regression, neural network, and principal component analysis methods. The results from this dissertation work also imply that the selection of the clustering algorithm does not significantly affect the calculated health value, and in general, most clustering algorithms appear suitable for detecting the problem using the residual clustering approach. The feasibility of the residual clustering approach is demonstrated in three case studies. For the wind speed sensor health monitoring case study, the residual clustering method provides the most accurate health assessment of the wind speed sensors when compared with the other methods used by the 24 participants in the Prognostics and Health Management 2011 Data Challenge. The residual clustering approach also outperformed other multi-regime health monitoring methods such as a mixture distribution overlap method for the gearbox case study. The residual clustering method was also able to provide an early detection of a problem on the wind turbine rotor shaft with 26 days of advanced warning. The rotor shaft health value using the residual clustering approach had the most monotonic health trend when compared with three other multi-regime health monitoring methods for the wind turbine drivetrain case study. The dissertation work shows that the residual clustering approach is fundamentally sound and should be considered along with the existing methods for multi-regime condition monitoring applications. The method appears to outperform many of the existing methods, and would be an appropriate monitoring algorithm if there is a nominal amount of correlation in the measured signals. Additional refinement of the approach can look into more sophisticated methods for threshold setting along with integrating a feature selection method into the residual clustering framework. In addition, algorithms for diagnosis and remaining useful life estimation for multi-regime condition monitoring applications would also require additional research and development work.
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This book presents the results of the research project G5055 'Development of novel methods for the prevention of pipeline failures with security implications,' carried out in the framework of the NATO Science for Peace and Security program, and explores the lifecycle assessment of gas infrastructures. Throughout their service lives, pipelines transporting hydrocarbons are exposed to demanding working conditions and aggressive media. In long-term service, material aging increases the risk of damage and failure, which can be accompanied by significant economic losses and severe environmental consequences. This book presents a selection of complementary contributions written by experts operating in the wider fields of pipeline integrity; taken together, they offer a comprehensive portrait of the latest developments in this technological area.