Data-Driven Fault Detection and Reasoning for Industrial Monitoring

Jing Wang

ISBN 10: 9811680434 / ISBN 13: 9789811680434

Verlag: Springer Nature Singapore, Springer Nature Singapore, 2022

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - 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.

Bestandsnummer des Verkäufers 9789811680434

Bibliografische Details

• Titel: Data-Driven Fault Detection and Reasoning for Industrial Monitoring
• Verlag: Springer Nature Singapore, Springer Nature Singapore
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 9811680434
• ISBN-13: 9789811680434
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 21 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 21 mm
• Gewicht: 594 Gramm

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

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.