Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems

Steven X. Ding

ISBN 10: 1447164091 / ISBN 13: 9781447164098

Published by Springer London Apr 2014, 2014

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and demonstrated on industrial case systems. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems will be of interest to process and control engineers, engineering students and researchers with a control engineering background. 320 pp. Englisch.

Seller Inventory # 9781447164098

Bibliographic Details

• Title: Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems
• Publisher: Springer London Apr 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 1447164091
• ISBN 13: 9781447164098
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 23D millimeters 241 millimeters width by 160 millimeters height by 23 millimeters depth
• Weight: 647 grams

About this title

Synopsis

Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and demonstrated on industrial case systems. Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems will be of interest to process and control engineers, engineering students and researchers with a control engineering background.

About the Author

In 1986, Steven X. Ding begun with the Ph D study/investigation on model-based FDI isusses. During his three years stay in industry, he had gained experiences with the applications of FDI techniques in real technical processes. In the last 17 years, he has been, as a university professor and institute head, involved in numerous national and international research grants and industrial projects in developing advanced FDI methods and their applications in different industrial sectors. He holds a lecture on fault diagnosis and fault-tolerant systems for master students and seminars on advanced FDI methods for PhD students at the University Duisburg-Essen and gives guest lectures/courses at other universities and research institutes. He has published more than 80 journal and 130 conference papers in this thematic area.