Download or read book Bayesian Networks in Fault Diagnosis written by Cai Baoping and published by World Scientific. This book was released on 2018-08-24 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This unique compendium presents bibliographical review on the use of BNs in fault diagnosis in the last decades with focus on engineering systems. Subsequently, eleven important issues in BN-based fault diagnosis methodology, such as BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification are discussed in various cases. Researchers, professionals, academics and graduate students will better understand the theory and application, and benefit those who are keen to develop real BN-based fault diagnosis system.
Download or read book Multi fault Diagnosis with Bayesian Networks written by Heng Zhou and published by . This book was released on 2004 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Networks written by Olivier Pourret and published by John Wiley & Sons. This book was released on 2008-04-30 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.
Download or read book Bayesian Networks for Reliability Engineering written by Baoping Cai and published by Springer. This book was released on 2019-02-28 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field of reliability. After focusing on the engineering systems, the book subsequently discusses twelve important issues in the BN-based reliability methodologies, such as BN structure modeling, BN parameter modeling, BN inference, validation, and verification. As such, it is a valuable resource for researchers and practitioners in the field of reliability engineering.
Download or read book Dynamic Bayesian Network Based Fault Diagnosis on Nonlinear Dynamic Systems written by Jiannian Weng and published by . This book was released on 2013 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Industrial Fault Detection and Diagnosis Using Bayesian Belief Network written by Hassan Gharahbagheri and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.
Download or read book Fault Diagnosis of Hybrid Systems with Dynamic Bayesian written by Noemí Moya Alonso and published by . This book was released on 2013 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning Bayesian Networks written by Richard E. Neapolitan and published by Prentice Hall. This book was released on 2004 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.
Download or read book Fault Detection with Bayesian Network written by Verron Sylvain and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this chapter, we show that a bayesian network can be an efficient way to diagnose a fault in multivariate processes. We have selected two statistical fault detection techniques (the T 2 chart and the MEWMA chart) and we have demonstrated that these charts can be viewed as a discriminant analysis and so can be implemented in a simple bayesian network. As the efficiency of bayesian network for the diagnosis of systems has already been demonstrated (Verron et al., 2006; Verron et al., 2007), the evident outlook of this work is the full study of the use of bayesian network in order to monitor and control a multivariate process (detection and diagnosis in the same network).
Download or read book Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis written by Uffe B. Kjærulff and published by Springer Science & Business Media. This book was released on 2007-12-20 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence. This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding.
Download or read book Bayesian Networks in R written by Radhakrishnan Nagarajan and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Networks in R with Applications in Systems Biology is unique as it introduces the reader to the essential concepts in Bayesian network modeling and inference in conjunction with examples in the open-source statistical environment R. The level of sophistication is also gradually increased across the chapters with exercises and solutions for enhanced understanding for hands-on experimentation of the theory and concepts. The application focuses on systems biology with emphasis on modeling pathways and signaling mechanisms from high-throughput molecular data. Bayesian networks have proven to be especially useful abstractions in this regard. Their usefulness is especially exemplified by their ability to discover new associations in addition to validating known ones across the molecules of interest. It is also expected that the prevalence of publicly available high-throughput biological data sets may encourage the audience to explore investigating novel paradigms using the approaches presented in the book.
Download or read book Data Driven Fault Detection and Reasoning for Industrial Monitoring written by Jing Wang and published by Springer Nature. This book was released on 2022-01-03 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.
Download or read book 2021 IEEE 19th International Conference on Industrial Informatics INDIN written by IEEE Staff and published by . This book was released on 2021-07-21 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: INDIN focuses on recent developments, deployments, technology trends, and research results in Industrial Informatics related fields from both industry and academia
Download or read book Modeling and Reasoning with Bayesian Networks written by Adnan Darwiche and published by Cambridge University Press. This book was released on 2009-04-06 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.
Download or read book Applied Smoothing Techniques for Data Analysis written by Adrian W. Bowman and published by OUP Oxford. This book was released on 1997-08-14 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.
Download or read book Advancements in Smart City and Intelligent Building written by Qiansheng Fang and published by Springer. This book was released on 2019-04-03 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book entitled “Advancements in Smart City and Intelligent Building” is the Proceedings of the International Conference on Smart City and Intelligent Building (ICSCIB 2018) held in Hefei, China, September 15-16, 2018. It contains 58 papers in total categorized into 8 different tracks, on Building Energy Efficiency, Construction Robot and Automation, Intelligent Community and Urban Safety, Intelligentialization of Heating Ventilation Air Conditioning System, Information Technology and Intelligent Transportation Systems, New Generation Intelligent Building Platform Techniques, Smart Home and Utility, and Smart Underground Space, which cover a wide range areas of smart cities and intelligent buildings. ICSCIB2018 provided an international forum for professionals, academics, and researchers to present the latest developments from interdisciplinary theoretical studies, computational algorithm developments and engineering applications in smart cities and smart buildings. This academic event featured many opportunities to network with colleagues from around the world in a wonderful environment. Its program covered invitation and presentations from scientists, researchers, and practitioners who have been working in the related areas to establish platforms for collaborative research projects in these fields. The conference invited leaders from industry and academia to exchange and share their experiences, present research results, explore collaborations and to spark new ideas, with the aim of developing new projects and exploiting new technology in these fields, and bridge theoretical studies and emerging applications in various science and engineering branches. This book addresses the recent development and achievement in the field of smart city and intelligent building. It is primarily intended for researchers and students for undergraduate and postgraduate programs in the background of multiple disciplines including computer science, information systems, information technology, automatic control and automation, electrical and electronic engineering, and telecommunications who wish to develop and share their ideas, knowledge and new findings in smart city and intelligent building.
Download or read book Risk Assessment and Decision Analysis with Bayesian Networks written by Norman Fenton and published by CRC Press. This book was released on 2018-09-03 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary mathematics, probability, and statistics as needed Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.
