Intelligent Industrial Alarm Systems: Advanced Analysis and Design Methods - Hardcover

Wang, Jiandong; Hu, Wenkai; Chen, Tongwen

Hardcover

ISBN 10: 9819765153 ISBN 13: 9789819765157

Publisher: Springer, 2024

Synopsis

This book fills a gap in existing literature by providing a comprehensive academic perspective on industrial alarm systems, offering systematic methodologies, practical techniques, and visual analytic tools for engineers to improve system performance and design. Modern industrial plants rely on computerized monitoring systems to track hundreds of process variables in real time, enabling operators to maintain safe and efficient conditions. Automatic industrial alarm systems play a crucial role in alerting operators to abnormalities, such as high vessel levels, that could lead to unsafe conditions if left unaddressed. While contemporary alarm systems can be plagued with issues like nuisance alarms, recent academic research has introduced advanced methodologies, like Markov chain theory and Bayesian estimation, to optimize alarm parameters and enhance system performance. By integrating these theoretical advancements into practical applications, the goal is to develop intelligent industrial alarm systems that leverage historical data and process knowledge to predict and prevent alarm floods, ultimately ensuring safer and more efficient plant operations.

About this title

About the Author

Jiandong Wang is presently a professor with the College of Electrical Engineering and Automation at the Shandong University of Science and Technology, Qingdao, Shandong Province, China. He received the B.E. degree in automatic control from Beijing University of Chemical Technology, Beijing, China, in 1997, and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Alberta, Canada, in 2003 and 2007, respectively. From 1997 to 2001, he was a control engineer with the Beijing Tsinghua Energy Simulation Company, Beijing, China. From February 2006 to August 2006, he was a visiting scholar at the Department of System Design Engineering at the Keio University, Japan. From December 2006 to October 2016, he was an assistant/associate/full professor with the College of Engineering, Peking University, Beijing, China. His research interests include process monitoring, system identification, optimal scheduling and their applications to industrial problems in the field of Electrical and Chemical Engineering.  He is a Senior Member of IEEE, and TaiShan Scholar in Shandong Province.

Wenkai Hu is currently a Professor at the School of Automation at China University of Geosciences, Wuhan, China. He received the B.Eng. and M.Sc. degrees in Power and Mechanical Engineering from Wuhan University, Wuhan, Hubei, China, in 2010 and 2012, respectively, and the Ph.D. degree in Electrical and Computer Engineering from the University of Alberta in 2016. He was a Post-Doctoral Fellow from October 2016 to September 2018, and a research associate from November 2018 to February 2019 at the University of Alberta. His research interests include advanced alarm monitoring, process control, and data mining for complex industrial processes. He is a Senior Member of IEEE.

Tongwen Chen is presently a Professor and Tier 1 Canada Research chair in Intelligent Monitoring and Control in the Department of Electrical and Computer Engineering at the University of Alberta, Edmonton, Canada. He received the B.Eng. degree in Automation and Instrumentation from Tsinghua University (Beijing) in 1984, and the M.A.Sc. and Ph.D. degrees in Electrical Engineering from the University of Toronto in 1988 and 1991, respectively. His research interests include computer and network-based control systems, process safety and alarm systems, and their applications to the process and power industries. He is a Fellow of IEEE, International Federation of Automatic Control (IFAC), the Royal Society of Canada, as well as the Canadian Academy of Engineering.

From the Back Cover

This book fills a gap in existing literature by providing a comprehensive academic perspective on industrial alarm systems, offering systematic methodologies, practical techniques, and visual analytic tools for engineers to improve system performance and design. Modern industrial plants rely on computerized monitoring systems to track hundreds of process variables in real time, enabling operators to maintain safe and efficient conditions. Automatic industrial alarm systems play a crucial role in alerting operators to abnormalities, such as high vessel levels, that could lead to unsafe conditions if left unaddressed. While contemporary alarm systems can be plagued with issues like nuisance alarms, recent academic research has introduced advanced methodologies, like Markov chain theory and Bayesian estimation, to optimize alarm parameters and enhance system performance. By integrating these theoretical advancements into practical applications, the goal is to develop intelligent industrial alarm systems that leverage historical data and process knowledge to predict and prevent alarm floods, ultimately ensuring safer and more efficient plant operations.

Edition details

• Publisher: Springer
• Publication date: 2024
• ISBN 10: 9819765153
• ISBN 13: 9789819765157
• Binding: Hardcover
• Language: English
• Number of pages: 439