-PDF Download- Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods EBOOK

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Author: Chris Aldrich

Publisher: Springer Science & Business Media

Published: 2013-06-15

Total Pages: 388

ISBN-13: 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.

Performance Assessment for Process Monitoring and Fault Detection Methods

Author: Kai Zhang

Publisher: Springer

Published: 2016-10-04

Total Pages: 164

ISBN-13: 3658159715

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The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes.

Advanced methods for fault diagnosis and fault-tolerant control

Author: Steven X. Ding

Publisher: Springer Nature

Published: 2020-11-24

Total Pages: 664

ISBN-13: 3662620049

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The major objective of this book is to introduce advanced design and (online) optimization methods for fault diagnosis and fault-tolerant control from different aspects. Under the aspect of system types, fault diagnosis and fault-tolerant issues are dealt with for linear time-invariant and time-varying systems as well as for nonlinear and distributed (including networked) systems. From the methodological point of view, both model-based and data-driven schemes are investigated.To allow for a self-contained study and enable an easy implementation in real applications, the necessary knowledge as well as tools in mathematics and control theory are included in this book. The main results with the fault diagnosis and fault-tolerant schemes are presented in form of algorithms and demonstrated by means of benchmark case studies. The intended audience of this book are process and control engineers, engineering students and researchers with control engineering background.

Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems

Author: Rui Yang

Publisher: CRC Press

Published: 2022-06-16

Total Pages: 93

ISBN-13: 1000594920

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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.

Process Monitoring and Fault Diagnosis Based on Multivariable Statistical Analysis

Author: Xiangyu Kong

Publisher: Springer Nature

Published:

Total Pages: 324

ISBN-13: 981998775X

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Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems

Author: Ruqiang Yan

Publisher: CRC Press

Published: 2024-06-06

Total Pages: 217

ISBN-13: 1040026591

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The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains. The book will appeal to academic researchers, practitioners, and students in the fields of intelligent fault diagnosis, prognostics and health management, and deep learning.

Time Series Analysis

Author: Chun-Kit Ngan

Publisher: BoD – Books on Demand

Published: 2019-11-06

Total Pages: 131

ISBN-13: 1789847788

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This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.

Artificial Intelligence in Process Fault Diagnosis

Author: Richard J. Fickelscherer

Publisher: John Wiley & Sons

Published: 2024-02-21

Total Pages: 436

ISBN-13: 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.

Artificial Intelligence in Models, Methods and Applications

Author: Olga Dolinina

Publisher: Springer Nature

Published: 2023-04-24

Total Pages: 694

ISBN-13: 303122938X

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This book is based on the accepted research papers presented in the International Conference "Artificial Intelligence in Engineering & Science" (AIES-2022). The aim of the AIES Conference is to bring together researchers involved in the theory of computational intelligence, knowledge engineering, fuzzy systems, soft computing, machine learning and related areas and applications in engineering, bioinformatics, industry, medicine, energy, smart city, social spheres and other areas. This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence.

Proceedings of the 15th International Conference on Vibration Problems

Author:

Publisher: Springer Nature

Published: 2024

Total Pages: 492

ISBN-13: 9819959225

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This book presents the Proceedings of the 15th International Conference on Vibration Problems (ICoVP 2023) and covers vibration problems of engineering both in theoretical and applied fields. Various topics covered in this volume are Vibration in Oil and Gas, Structural Dynamics, Structural Health Monitoring, Rotor Dynamics, Measurement Diagnostics in Vibration, Computational methods in Vibration and Wave Mechanics, Dynamics of Coupled Systems, Dynamics of Micro and Macro Systems, Multi-body dynamics, Nonlinear dynamics Reliability of dynamic systems, Vibrations due to solid/liquid phase interaction, Vibrations of transport systems, Seismic Isolation, Soil dynamics, Geotechnical earthquake engineering Dynamics of concrete structures, Underwater shock waves (Tsunami), Vibration control, uncertainty quantification and reliability analysis of dynamic structures, Vibration problems associated with nuclear power reactors, Earthquake engineering, impact and wind loading and vibration in composite structures and fracture mechanics. This book will be useful for both professionals and researchers working on vibrations problems in multidisciplinary areas.