[PDF] Artificial Intelligence In Process Fault Diagnosis eBook

Author : Richard J. Fickelscherer
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 37,2 MB
Release : 2024-01-23
Category : Science
ISBN : 1119825911

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Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading experts Guidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and more Comprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.

Author : Chris Aldrich
Publisher : Springer Science & Business Media
Page : 388 pages
File Size : 21,9 MB
Release : 2013-06-15
Category : Computers
ISBN : 1447151852

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This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Author : Józef Korbicz
Publisher : Springer Science & Business Media
Page : 936 pages
File Size : 47,24 MB
Release : 2012-12-06
Category : Computers
ISBN : 3642186157

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This comprehensive work presents the status and likely development of fault diagnosis, an emerging discipline of modern control engineering. It covers fundamentals of model-based fault diagnosis in a wide context, providing a good introduction to the theoretical foundation and many basic approaches of fault detection.

Author : Jingzheng Ren
Publisher : Elsevier
Page : 542 pages
File Size : 28,64 MB
Release : 2021-06-05
Category : Technology & Engineering
ISBN : 012821743X

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Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering

Author : Richard J. Fickelscherer
Publisher : John Wiley & Sons
Page : 436 pages
File Size : 48,78 MB
Release : 2024-02-21
Category : Science
ISBN : 111982589X

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Artificial Intelligence in Process Fault Diagnosis A comprehensive guide to the future of process fault diagnosis Automation has revolutionized every aspect of industrial production, from the accumulation of raw materials to quality control inspections. Even process analysis itself has become subject to automated efficiencies, in the form of process fault analyzers, i.e., computer programs capable of analyzing process plant operations to identify faults, improve safety, and enhance productivity. Prohibitive cost and challenges of application have prevented widespread industry adoption of this technology, but recent advances in artificial intelligence promise to place these programs at the center of manufacturing process analysis. Artificial Intelligence in Process Fault Diagnosis brings together insights from data science and machine learning to deliver an effective introduction to these advances and their potential applications. Balancing theory and practice, it walks readers through the process of choosing an ideal diagnostic methodology and the creation of intelligent computer programs. The result promises to place readers at the forefront of this revolution in manufacturing. Artificial Intelligence in Process Fault Diagnosis readers will also find: Coverage of various AI-based diagnostic methodologies elaborated by leading experts Guidance for creating programs that can prevent catastrophic operating disasters, reduce downtime after emergency process shutdowns, and more Comprehensive overview of optimized best practices Artificial Intelligence in Process Fault Diagnosis is ideal for process control engineers, operating engineers working with processing industrial plants, and plant managers and operators throughout the various process industries.

Author : Md Shafiullah
Publisher : Elsevier
Page : 430 pages
File Size : 35,64 MB
Release : 2022-01-14
Category : Technology & Engineering
ISBN : 032388430X

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Power System Fault Diagnosis: A Wide Area Measurement Based Intelligent Approach is a comprehensive overview of the growing interests in efficient diagnosis of power system faults to reduce outage duration and revenue losses by expediting the restoration process.This book illustrates intelligent fault diagnosis schemes for power system networks, at both transmission and distribution levels, using data acquired from phasor measurement units. It presents the power grid modeling, fault modeling, feature extraction processes, and various fault diagnosis techniques, including artificial intelligence techniques, in steps. The book also incorporates uncertainty associated with line parameters, fault information (resistance and inception angle), load demand, renewable energy generation, and measurement noises. Provides step-by-step modeling of power system networks (distribution and transmission) and faults in MATLAB/SIMULINK and real-time digital simulator (RTDS) platforms Presents feature extraction processes using advanced signal processing techniques (discrete wavelet and Stockwell transforms) and an easy-to-understand optimal feature selection method Illustrates comprehensive results in the graphical and tabular formats that can be easily reproduced by beginners Highlights various utility practices for fault location in transmission networks, distribution systems, and underground cables.

Author : Vasile Palade
Publisher : Springer Science & Business Media
Page : 374 pages
File Size : 42,66 MB
Release : 2006-12-22
Category : Computers
ISBN : 184628631X

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This book presents the most recent concerns and research results in industrial fault diagnosis using intelligent techniques. It focuses on computational intelligence applications to fault diagnosis with real-world applications used in different chapters to validate the different diagnosis methods. The book includes one chapter dealing with a novel coherent fault diagnosis distributed methodology for complex systems.

Author : Richard J. Fickelscherer
Publisher : John Wiley & Sons
Page : 218 pages
File Size : 42,40 MB
Release : 2012-11-27
Category : Technology & Engineering
ISBN : 1118481968

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Tested and proven strategy to develop optimal automated process fault analyzers Process fault analyzers monitor process operations in order to identify the underlying causes of operational problems. Several diagnostic strategies exist for automating process fault analysis; however, automated fault analysis is still not widely used within the processing industries due to problems of cost and performance as well as the difficulty of modeling process behavior at needed levels of detail. In response, this book presents the method of minimal evidence (MOME), a model-based diagnostic strategy that facilitates the development and implementation of optimal automated process fault analyzers. MOME was created at the University of Delaware by the researchers who developed the FALCON system, a real-time, online process fault analyzer. The authors demonstrate how MOME is used to diagnose single and multiple fault situations, determine the strategic placement of process sensors, and distribute fault analyzers within large processing systems. Optimal Automated Process Fault Analysis begins by exploring the need to automate process fault analysis. Next, the book examines: Logic of model-based reasoning as used in MOME MOME logic for performing single and multiple fault diagnoses Fuzzy logic algorithms for automating MOME Distributing process fault analyzers throughout large processing systems Virtual SPC analysis and its use in FALCONEERTM IV Process state transition logic and its use in FALCONEERTM IV The book concludes with a summary of the lessons learned by employing FALCONEERTM IV in actual process applications, including the benefits of "intelligent supervision" of process operations. With this book as their guide, readers have a powerful new tool for ensuring the safety and reliability of any chemical processing system.

Author : Marco Antonio Aceves-Fernandez
Publisher : BoD – Books on Demand
Page : 466 pages
File Size : 38,9 MB
Release : 2018-06-27
Category : Computers
ISBN : 178923364X

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Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area.