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Special Issue: Data-based control and process monitoring with industrial applications

Modern industry-related applications such as industrial electronics, business systems, public sectors etc. generate about over one thousand Exabyte annually, and a 20-fold increase is expected in the next ten years. The arrival of a data epoch has made it more important than ever to use and interpret the embedded information.

Efficient capture and analysis of the available data have the potential to enhance productivity and competitiveness in a wide range of electrical and electronics sectors. Instead of seeking accurate physical system models, engineers prefer the advanced model-free techniques in system control and monitoring in order to optimize the performance of the whole process by basing it only on the large amounts of measured data. Moreover, practical control systems in other industrial fields such as supply chain management and risk management can be further improved by employing big data solutions. Based on these observations, novel approaches focused on data are imperative and critical to the future developments of modern industry. The common aim of data-based control and process monitoring with industrial applications is the effective utilization of huge amounts of measured or stored data to achieve the normal running and the desired performance of the processes under consideration. The objective of this Special Issue is to provide the recent advances of data-based system control and monitoring approaches in modern industrial applications. The papers should contain both theoretical and practical/experimental results.

Topics to be covered in this special issue include, but are not limited to the following: n Novel techniques for data analysis and mining n Data based fault diagnosis and healthy monitoring n Data based modeling and system identification n Data based solution for supply chain and risk management system n Data based solution focused on control and process monitoring n Big data challenges and the future of industry n Data based plant-wide optimization and prognosis n Data based applications for modern industrial systems

All submissions are subject to the journal’s peer-review procedures. The authors should follow the journal’s Author Guide at http://digital-library.theiet.org/journals/author-guide when preparing papers for submission to the Special Issue.

Special Issue Guest Editors: Important dates: Prof. Huijun Gao Prof. Shen Yin Research Institute of Intelligent Research Institute of Control and Systems Mechatronics and Automation Harbin Institute of Bohai Submission of Manuscript China E: [email protected] [email protected] E: [email protected] 30 June 2014 T: +86-451-86402351 ext. 4121 T: +86-416-3400005

Notification of Acceptance Prof. Steven X. Ding Dr. Zhuo Wang Institute for Automatic Control Department of Electrical and 1 July 2014 and Complex Systems (AKS) Computer University of Duisburg-Essen University of Alberta Germany Edmonton, Alberta Final Manuscript Due E: [email protected] E: [email protected] 1 September 2014 T: +49-203-379-3385

Tentative Publication Date All papers must be submitted through the journal’s Manuscript Central system: February 2015 http://mc.manuscriptcentral.com/iet-cta

Turnmore over infofor About our Special Issue Guest Editors

Prof. Huijun Gao Prof. Steven X. Ding Prof. Shen Yin Prof. Huijun Gao received the Ph.D. degree Prof. Steven Ding received Ph.D. degree Prof. Shen Yin received his B.E. degree in control and engineering from the in electrical engineering from the Gerhard- in automation from Harbin Institute of Harbin Institute of Technology, Harbin, China, Mercator University of Duisburg, Germany, Technology, China, M.Sc. degree in control in 2005. He was a Research Associate with in 1992. From 1992 to 1994, he was a and information system and the Ph.D. degree the Department of Mechanical Engineering, R&D engineer at Rheinmetall GmbH. From in electrical engineering and information The University of Hong Kong, from November 1995 to 2001, he was a professor of control technology from University of Duisburg-Essen, 2003 to August 2004. engineering at the University of Applied Germany. He is currently a Professor and Science Lausitz in Senftenberg, Germany, serves as Dean for school of engineering at From October 2005 to October 2007, he and served as vice president of this university Bohai University, China. conducted his postdoctoral research with during 1998 to 2000. the Department of Electrical and Computer His research interests are model based and Engineering, University of Alberta, AB, Since 2001, he has been a professor of data-driven prognosis, fault diagnosis, fault Canada. Since November 2004, he has been control engineering and the head of the tolerant control and big data focused on with the Harbin Institute of Technology, where Institute for Automatic Control and Complex industrial applications. He serves as the he is currently a Professor and Director of the Systems (AKS) at the University of Duisburg- Chair of Technical Committee “Data driven Research Institute of Intelligent Control and Essen, Germany. His research interests are Control and Monitoring” at the IEEE Industrial Systems. His research interests include data- model-based and data-driven fault diagnosis, Electronics . driven process control, network-based control, fault-tolerant systems and their application in robust control/filter theory, time-delay systems, different industrial sectors. Dr. Zhuo Wang and their engineering applications. Dr. Zhuo Wang received his B.E. degree in automation from Beihang University, Dr Gao is an Associate Editor for of the China and his Ph.D. degree in electrical following publications: Automatica, the IEEE and computer engineering from University Transactions on Industrial Electronics, the of Illinois at Chicago, USA. He is currently IEEE Transactions on Cybernetics, the IEEE a Postdoctoral fellow in the Department Transactions on Mechatronics, the IEEE of Electrical and Computer Engineering at Transactions on Fuzzy Systems, and the IEEE University of Alberta, Canada. Transactions on Control Systems Technology. His research interests are data-driven systems analysis and controller design, artificial neural networks, model-free adaptive control, and event-trigger control.

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