On Line Fault Diagnosis Of Industrial Processes Based On Artificial Intelligence Techniques
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Artificial Intelligence in Process Fault Diagnosis
| Author | : Richard J. Fickelscherer |
| Publisher | : John Wiley & Sons |
| Total Pages | : 436 |
| Release | : 2024-02-21 |
| Genre | : Science |
| ISBN | : 111982589X |
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.
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
| Author | : Chris Aldrich |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 388 |
| Release | : 2013-06-15 |
| Genre | : Computers |
| ISBN | : 1447151852 |
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.
Fault Diagnosis
| Author | : Józef Korbicz |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 936 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 3642186157 |
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.
Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems
| Author | : Rui Yang |
| Publisher | : CRC Press |
| Total Pages | : 87 |
| Release | : 2022-06-16 |
| Genre | : Technology & Engineering |
| ISBN | : 1000594939 |
This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery. Techniques and applications through experiments and case studies for intelligent precision compensation and fault diagnosis are offered along with the introduction of machine learning and deep learning methods. Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems discusses how to formulate and solve precision compensation and fault diagnosis problems. The book includes experimental results on hardware equipment used as practical examples throughout the book. Machine learning and deep learning methods used in intelligent precision compensation and intelligent fault diagnosis are introduced. Applications to deal with relevant problems concerning CNC machining and rotating machinery in industrial engineering systems are provided in detail along with applications used in precision motion systems. Methods, applications, and concepts offered in this book can help all professional engineers and students across many areas of engineering and operations management that are involved in any part of Industry 4.0 transformation.
Real Time Fault Monitoring of Industrial Processes
| Author | : A.D. Pouliezos |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 571 |
| Release | : 2013-03-09 |
| Genre | : Technology & Engineering |
| ISBN | : 9401583005 |
This book presents a detailed and up-to-date exposition of fault monitoring methods in industrial processes and structures. The following approaches are explained in considerable detail: Model-based methods (simple tests, analytical redundancy, parameter estimation); knowledge-based methods; artificial neural network methods; and nondestructive testing, etc. Each approach is complemented by specific case studies from various industrial sectors (aerospace, chemical, nuclear, etc.), thus bridging theory and practice. This volume will be a valuable tool in the hands of professional and academic engineers. It can also be recommended as a supplementary postgraduate textbook. For scientists whose work involves automatic process control and supervision, statistical process control, applied statistics, quality control, computer-assisted predictive maintenance and plant monitoring, and structural reliability and safety.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
| Author | : Jing Wang |
| Publisher | : Springer |
| Total Pages | : 264 |
| Release | : 2022-01-04 |
| Genre | : Technology & Engineering |
| ISBN | : 9789811680434 |
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.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
| Author | : Jing Wang |
| Publisher | : Springer Nature |
| Total Pages | : 277 |
| Release | : 2022-01-03 |
| Genre | : Technology & Engineering |
| ISBN | : 9811680442 |
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.
Filter-Based Fault Diagnosis and Remaining Useful Life Prediction
| Author | : Yong Zhang |
| Publisher | : CRC Press |
| Total Pages | : 290 |
| Release | : 2023-02-10 |
| Genre | : Technology & Engineering |
| ISBN | : 1000835944 |
This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning. Features: Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities. Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective. Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system. Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems. Includes simulation examples in each chapter to reflect the engineering practice. This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.
Computational Intelligence in Fault Diagnosis
| Author | : Vasile Palade |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 374 |
| Release | : 2006-12-22 |
| Genre | : Computers |
| ISBN | : 184628631X |
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.
