Download or read book System Level Prognosis and Health Monitoring Modeling Framework and Software Implementation for Gas Pipeline System Integrity Management written by Wadie cHALGHAM and published by . This book was released on 2020 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Reliability Based Integrity Management of Natural Gas Pipelines Subject to Spatio Temporal Corrosive Environment written by Keo-Yuan Wu and published by . This book was released on 2020 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Prognostics and Health Management written by Douglas Goodman and published by John Wiley & Sons. This book was released on 2019-04-01 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Model based Health Monitoring of Hybrid Systems written by Danwei Wang and published by Springer. This book was released on 2013-10-18 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically presents a comprehensive framework and effective techniques for in-depth analysis, clear design procedure, and efficient implementation of diagnosis and prognosis algorithms for hybrid systems. It offers an overview of the fundamentals of diagnosis\prognosis and hybrid bond graph modeling. This book also describes hybrid bond graph-based quantitative fault detection, isolation and estimation. Moreover, it also presents strategies to track the system mode and predict the remaining useful life under multiple fault condition. A real world complex hybrid system—a vehicle steering control system—is studied using the developed fault diagnosis methods to show practical significance. Readers of this book will benefit from easy-to-understand fundamentals of bond graph models, concepts of health monitoring, fault diagnosis and failure prognosis, as well as hybrid systems. The reader will gain knowledge of fault detection and isolation in complex systems including those with hybrid nature, and will learn state-of-the-art developments in theory and technologies of fault diagnosis and failure prognosis for complex systems.
Download or read book Diagnostics and Prognostics of Engineering Systems Methods and Techniques written by Kadry, Seifedine and published by IGI Global. This book was released on 2012-09-30 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Industrial Prognostics predicts an industrial systems 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 systems 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 methods 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.
Download or read book Pipeline Integrity Handbook written by Ramesh Singh and published by Gulf Professional Publishing. This book was released on 2013-09-18 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Gas Pipeline Safety written by United States Government Accountability Office and published by Createspace Independent Publishing Platform. This book was released on 2018-05-29 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gas Pipeline Safety: Preliminary Observations on the Implementation of the Integrity Management Program
Download or read book Mitigation of Gas Pipeline Integrity Problems written by Mavis Sika Okyere and published by CRC Press. This book was released on 2020-10-04 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Prognostics and Health Assessment of a Multi regime System Using a Residual Clustering Health Monitoring Approach written by David Siegel and published by . This book was released on 2013 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Gas Pipeline Safety written by United States Government Accountability and published by Scholar's Choice. This book was released on 2015-02-14 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Download or read book Degradation Assessment and Failure Prevention of Pipeline Systems written by Gabriella Bolzon and published by Springer Nature. This book was released on 2020-09-10 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Gas Pipeline Safety written by Katherine Siggerud and published by . This book was released on 2006 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Enhancement of Physics of Failure Prognostic Models with System Level Features written by and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: To truly optimize the deployment of DoD assets, there exists a fundamental need for predictive tools that can reliably estimate the current and reasonably predict the future capacity of complex systems. Prognosis, as in all true predictions, has inherent uncertainty, which has been treated through probabilistic modeling approaches. The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring data and various inspection results. Regardless of the fidelity of a prognostic model or the quantity and quality of the seeded fault or run-to-failure data, these models should be adaptable based on system health features such as vibration, temperature, and oil analysis. The inherent uncertainties and variability in material capacity and localized environmental conditions, as well as the realization that complex physics-of-failure, understanding will always possess some uncertainty, all contribute to the stochastic nature of prognostic modeling. However, accuracy can be improved by creating a prognostic architecture instilled with the ability to account for unexpected damage events, fuse with diagnostic results, and statistically calibrate predictions based on inspection information and real-time system level features. In this paper, the aforementioned process is discussed and implemented first on controlled failures of single spur gear teeth and then on a helical gear contained within a drivetrain system. The stochastic, physics-of-failure models developed are validated with transitional run-to-failure data developed at Penn State ARL. Future work involves applying the advanced prognostics process to helicopter gearboxes.
Download or read book Development of Probabilistic Corrosion Growth Models with Applications in Integrity Management of Pipelines written by Shenwei Zhang and published by . This book was released on 2014 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metal-loss corrosion is a major threat to the structural integrity and safe operation of underground oil and gas pipelines worldwide. The reliability-based corrosion management program has been increasingly used in the pipeline industry, which typically includes three tasks, namely periodic high-resolution inline inspections (ILIs) to detect and size corrosion defects on a given pipeline, engineering critical assessment of the corrosion defects reported by the inspection tool and mitigation of defects. This study addresses the core involved in the reliability-based corrosion management program. First, the stochastic process in conjunction with the hierarchical Bayesian methodology is used to characterize the growth of defect depth using imperfect ILI data. The biases, random scattering errors as well as the correlations between the random scattering errors associated with the ILI tools are accounted for in the Bayesian inference. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to carry out the Bayesian updating and numerically evaluate the posterior distributions of the parameters in the growth model. Second, a simulation-based methodology is presented to evaluate the time-dependent system reliability of pressurized energy pipelines containing multiple active metal-loss corrosion defects using the developed growth models. Lastly, a probabilistic investigation is carried out to determine the optimal inspection interval for the newly-built onshore underground natural gas pipelines with respect to external metal-loss corrosion by considering the generation of corrosion defects over time and time-dependent growth of individual defects. The proposed methodology will facilitate the reliability-based corrosion management for corroding pipelines.
Download or read book An Adaptive Prognostic Methodology and System Framework for Engineering Systems Under Dynamic Working Regimes written by Shanhu Yang and published by . This book was released on 2016 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prognostics and Health Management (PHM) as a research discipline focuses on assessing degradation behavior and predicting time to failure of an engineering system with condition monitoring data collected throughout the lifespan of the system. The information of predicted Remaining Useful Life (RUL) and potential failure modes further enables just-in-time maintenance, reduced operational cost and optimized production. In recent years with the development of information systems such as cloud computing and Internet of Things (IOT), machine data from factory floors can be collected more conveniently with higher speed, volume and variety, which brings about new opportunities and much wider application of PHM technologies. On the other hand, the emerging industrial big data with real world complications also imposes greater challenges to the PHM research community. Data collected from a large amount of machine units under dynamic working regimes requires algorithms to adaptively and autonomously recognize and handle different situations. Autonomous PHM algorithms can further be implemented in centralized computing platforms for more efficient, faster and large scale data mining and analytics, which will eventually lead to more effective handling and exploitation of industrial big data. PHM algorithms have been developed based on specific applications and datasets. In addition, most of PHM tools are developed based on limited working regimes. In reality, many engineered machinery and systems often work under different dynamic working regimes and as a consequence it is always a challenge to implement PHM in such conditions. This dissertation work presents the development of a systematically designed and implementation-ready methodology for adaptive health assessment and prognostics for real world machine fleets that undergo dynamic working regimes and other complications. Due to limitations in data and knowledge for in-field systems, the approach assumes no prior knowledge or available training data and attempts to extract degradation information only from condition monitoring data streamed in real time. The approach contains a generalized state space model for machine degradation and an adaptive and online methodology for real time degradation assessment and prediction. The degradation model is a generalized yet comprehensive description of the relationships among the three key aspects in the PHM related research, which are system degradation, system measurements and working regimes. The online methodology further consists of an adaptive segmentation method for identification of health stages based on local variation, a variable selection algorithm for selecting related working regime parameters and an Adaptive Kalman Filter (AKF) based online filtering method for model identification and prediction. The methodology is demonstrated and validated using both simulated data and data from real world industrial applications. The case studies show that the proposed approach is able to deliver robust and accurate results with little algorithm tuning needed for different applications, which is ideal for facilitating automated data processing and analytics in online PHM platforms.
Download or read book Data driven Modeling and Prognosis of Condition Monitoring Signals in Engineering Systems written by Raed Al Kontar and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Condition monitoring (CM) data, or simply monitoring data, is defined as a dataset that has been collected from individuals along the time, and it implicitly manifests the underlying unobservable system status. Due to the advances in sensory devices and information technology, prognosis and diagnosis in various fields can take enormous advantages from the rich condition monitoring data. However, at the same time, it also creates new challenges for research in data analytics as to how this vast and complex data could be utilized to retrieve accurate diagnosis and meaningful prognosis. Many existing techniques fall short of addressing this issue because most of them are developed when the data were collected in a well-controlled experimental setting. However, the monitoring data often involves many factors that are uncontrollable and, inevitably, has severe heterogeneity. This research simultaneously addresses multiple challenges that arise from the monitoring data. i. An Individualized model is crucial for effective diagnosis and prognosis based on the monitoring data. The primary focus of collecting the monitoring data is to understand the specific in-service unit rather than studying the population behavior. Therefore, the predictions or diagnostic decisions based on the monitoring data needs to be highly individualized. Collecting monitoring data happens in the on-line stage at the real time. Thus, the model should be able to update or adjust itself according to the newly-collected data points from the specific individual. ii. A non-parametric framework that can account for heterogeneity and handle high dimensionality in the data is needed. CM signals may not follow any parametric form, and if the specified form is far from the truth, the modeling and prognosis results will be misleading. For instance, parametric representations are typically based on physical or chemical theories; however, in most cases, such theories are unknown. Therefore, functional forms should be derived through empirical evaluation or visual observation, making them sensitive to model misspecification iii. A flexible modeling strategy that can handle multiple data types simultaneously is needed. Specifically incorporating qualitative data and introducing a distance measure between such data is essential for better prognosis. The monitoring data comes in various data types. In the literature, there are many statistical methods that are developed for a specific type of data which is not suitable for the monitoring data that includes various data types at the same time. Thus, a novel statistical model fusion needs to be investigated. iv. A scalable approach specifically when the number of CM signals/functional outputs is large. Further, the integrative analysis of multiple outputs implicitly assumes that these outputs share some commonalities. However, if this does not hold, negative transfer of knowledge may occur, which leads to decreased performance relative to learning tasks separately. Therefore, the model needs to possess excellent scalability when the number of outputs is large and simultaneously minimizes the negative transfer of knowledge between uncorrelated outputs. To address those issues listed above, four tasks are investigated in this report. (a) To build a mixture mixed effects model which is able to account for imbalance (early vs late failure) in the data. This technique greatly improves prognostics specifically for systems where most units are reliable and only few tend to fail at early stages of their life cycle. (b) To propose an alternative view on modeling CM data using multivariate Gaussian process. This view draws its roots from multitask learning and is based on treating each CM signal as an individual task. This technique is non-parametric, scalable and is able to account for heterogeneity in the data. (c) To incorporate qualitative features in non-parametric prognostics through a reparametrization technique called hypersphere decomposition. This technique allows incorporating external factors into prognostic models through defining a distance measure based on a unit hypercube. (d) To provide scalability for the multivariate Gaussian process when the number of outputs is large and to minimize the negative transfer of knowledge between uncorrelated outputs. This technique utilizes a distributed estimation scheme which allows scaling to arbitrarily large datasets through parallelization.
Download or read book Data driven Prognosis and Diagnosis of Event Occurrences with Applications in Manufacturing and Healthcare Systems written by Junbo Son and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information technology revolution is turning modern engineering systems into smart and connected systems and such systems have become increasingly available in practice. Due to the advances in implementation of smart and connected systems, we now have massive data with rich condition monitoring signals of in-situ systems and detailed records of critical events. This unprecedented data availability realized by the smart and connected systems provides significant opportunities for sophisticated data-driven prognosis and diagnosis for the underlying health status of a system in various fields. Successful prognosis and diagnosis can prevent catastrophic consequences in advance and provide meaningful information about the underlying health status of a system. However, at the same time, it also creates new challenges for research in data analytics as to how this vast and complex data could be utilized to retrieve accurate prognosis and meaningful diagnosis. Many existing techniques fall short of addressing this issue because most of them are for the cases where the data were collected in a well-controlled experimental setting. The critical event records and condition monitoring data obtained from the complex smart and connected systems often involve many factors that are uncontrollable and inevitably exhibit severe heterogeneity. This thesis addresses multiple challenges for prognosis and diagnosis based on such data by establishing a series of data-driven methodologies. (a) To build a joint model framework for both time-to-failure data and condition monitoring signals by integrating Cox regression and mixed-effects model. (b) To extend the joint model framework to address various issues in the prognosis based on the monitoring data. (c) Establishing a joint prognostic model for recurrent events by hierarchically integrating logistic regression and mixed-effects models. (d) To establish a diagnostic model based on recurrent event data using correlated Gamma-based hidden Markov model. The proposed methods can be applied to a broad range of data analytics applications, and the emerging challenges in monitoring data obtained from smart and connected systems can be effectively addressed.
