Gebundenes Buch

Spatio-Temporal Abnormality Diagnosis for Industrial Distributed Parameter Systems

Model-Based and Data-Driven Perspectives

Versandkostenfrei!

Versandfertig in 6-10 Tagen

106,99 €

inkl. MwSt.

Jetzt bewerten

PAYBACK Punkte

53 °P sammeln!

Weiterlesen / Aufklappen

This book introduces recent developments and trends of S-T abnormality diagnosis for industrial distributed parameter systems (DPSs). As a typical representative of industrial processes, DPSs widely exist in both process and discrete manufacturing and operations, such as the snap curing oven in chip manufacturing, the tubular reactor in chemical manufacturing, the soft robots in special operations etc. With the increasing development of industrial distributed parameter systems (especially the electrical vehicles and hybrid electric vehicles), spatio-temporal (S-T) abnormality diagnosis has become a pain in the neck and has attracted a great amount of attention in recent years. Moreover, the rapid development of machine learning and big data techniques has shed new insights on data-driven fault diagnosis and promoted the enthusiasm for studying the industrial distributed parameter systems. However, nearly no book has addressed this issue well and most existing research only consider traditional actuator/sensor fault while neglecting the spatio-temporal distributed characteristic of abnormality for DPSs.

The main contents of this book include: 1) Model-based abnormality diagnosis and identification for completely-known industrial DPSs ( white box ); 2) Combined model-based and data-driven abnormality detection and localization for partially-known industrial DPSs ( grey box ); 3) Purely data-driven modeling and diagnosis for completely-unknown DPSs( black box ). In conclusion, this book summarizes the authors works on both model-based and data-driven perspectives for S-T abnormality diagnosis of industrial DPSs. To be more precise, this book mainly focuses on the following challenges: space-time couple characteristics, limited sensing in space, and the dynamically varying abnormality in space. This book aims at post-graduate students, researchers, and engineers with background knowledge of industrial systems modeling and monitoring. Interestingreaders can obtain state-of-the-art methods systematically in the last 5 years and have a general overview of recent developments and the future direction of this specific research field.

Weniger Details

Yun Feng received the B.E. degree in automation and the M.S. degree in control theory and control engineering from the Department of Automation, Wuhan University, Wuhan, China, in 2014 and 2017, respectively, and the Ph.D. degree from the Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, in 2020. From July to November 2019, he was a Visiting Student with the Institute for Automatic Control and Complex Systems (AKS), University of Duisburg Essen, Duisburg, Germany. He is currently an Associate Professor with the School of Artificial Intelligence and Robotics and the National Engineering Research Center for Robot Visual Perception and Control Technology, Hunan University, Changsha, China. He has authored or co-authored more than 50 papers in peer-reviewed international journals and conferences. Among them 20 papers are in the field closely related to the topic of this book. His research interests include spatial-temporal dynamical systems, fault diagnosis, and soft robotics. He was a recipient of the Excellent Young Scientists Fund of the National Natural Science Foundation of China, the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology, the Senior Member of the Chinese Association of Automation (CAA). He is serving as an Associate Editor for IEEE SYSTEMS, MAN, AND CYBERNETICS MAGAZINE and FRANKLIN OPEN, a youth editorial board member of ROBOT LEARNING. He is a Senior Member of the IEEE. Han-Xiong Li received the B.E. degree in aerospace engineering from the National University of Defense Technology, Changsha, China, in 1982, the M.E. degree in electrical engineering from the Delft University of Technology, Delft, The Netherlands, in 1991, and the Ph.D. degree in electrical engineering from The University of Auckland, Auckland, New Zealand, in 1997. He is currently a Chair Professor with the Department of Systems Engineering, City University of Hong Kong, Hong Kong. He has a broad experience in both academia and industry. He has authored two books, holds about 20 patents, and has published more than 250 SCI journal articles with H-index 55 (web of science). He has been rated as a Highly Cited Scholar in China by Elsevier since 2014. His current research interests include process modeling and control, distributed parameter systems, and system intelligence. Dr. Li was awarded the Distinguished Young Scholar (overseas) by the China National Science Foundation in 2004, a Chang Jiang Professorship by the Ministry of Education, China, in 2006, and a National Professorship in the China Thousand Talents Program in 2010. He serves as an Associate Editor for the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS and was an Associate Editor of the IEEE TRANSACTIONS ON CYBERNETICS from 2002 to 2016 and the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS from 2009 to 2015. He is a Fellow of the IEEE. Yaonan Wang received the B.S. degree in computer engineering from the East China University of Science and Technology, Fuzhou, China, in 1981, and the M.S. and Ph.D. degrees in control engineering from Hunan University, Changsha, China, in 1990 and 1994, respectively. He was a Post-Doctoral Research Fellow with the National University of Defense Technology, Changsha, from 1994 to 1995, a Senior Humboldt Fellow in Germany from 1998 to 2000, and a Visiting Professor with the University of Bremen, Bremen, Germany, from 2001 to 2004. He has been a Professor with Hunan University since 1995. His current research interests include robot control, intelligent control and information processing, industrial process control, and image processing. Dr. Wang has been a member of the China Engineering Academy since 2019.