Data-Driven Fault Detection and Reasoning for Industrial Monitoring (Intelligent Control and Learning Systems, 3) - Hardcover

Wang, Jing; Zhou, Jinglin; Chen, Xiaolu

Hardcover

ISBN 10: 9811680434 ISBN 13: 9789811680434

Publisher: Springer, 2022

Synopsis

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.

About this title

About the Author

Jing Wang received the B.S. degree in Industry Automation and the Ph.D. degree in Control Theory and Control Engineering from the Northeastern University, in 1994 and 1998, respectively. She was a professor with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China, from 1999 to 2020, and a visiting professor at University of Delaware, USA, in 2014. Now she is a professor with School of Electrical and Control Engineering, North China University of Technology, Beijing, China. Her research interest is oriented to different aspects, including modeling, optimization, advance control, process monitoring, and fault diagnosis for complex industrial process; industrial artificial intelligence based on analysis and learning from big data.

Jinglin Zhou received the B.Eng., M.Sc., and Ph.D. degrees from Daqing Petroleum Institute, Hunan University, Changsha, China, and the Institute of Automation, Chinese Academyof Sciences, Beijing, China, in 1999, 2002, and 2005, respectively. He was Academic Visitor with the Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK. He is currently Professor with the College of Information Science and Technology, Beijing University of Chemical Technology, Beijing. His current research interests include stochastic distribution control, fault detection and diagnosis, variable structure control, and their applications.

Xiaolu Chen received the Ph.D. degree in Control Science and Engineering from Beijing University of Chemical Technology in 2021. She was a joint PhD student at the University of Duisburg Essen, Duisburg, Germany, from 2019 to 2020. Now she is a postdoctoral fellow at Peking University, Beijing, China.  Her major is  control science and engineering.  Her research interests include modelling and fault diagnosis of complex industrial processes, data causality analysis, and intelligent learning algorithms.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2022
• Language: English
• ISBN 10: 9811680434
• ISBN 13: 9789811680434
• Binding: Hardcover
• Edition number: 1
• Number of pages: 281