Keynote Speech 01——Professor Dr.-Ing. S. X. Ding

Towards control-theoretically explainable diagnosis of faults in dynamic control systems

Professor Dr.-Ing. S. X. Ding

Institute of Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany

        Abstract：The model-based technique for fault diagnosis (FDI) in dynamic control systems is well-established, although data-driven and machine learning (ML) algorithms build the mainstream in research in recent years. The model-based FDI framework is established on the basis of input-output models of the system under consideration and rigorous application of control-theoretic methods. Differently, data-driven and ML-based algorithms are mainly focused on feature generation from the data space, and on modelling of the considered system operations by means of, technically speaking, identification and optimisation algorithms, known as training and learning as well. In particular, integration of various metrics known in statistics and optimisation theory in the loss functions for training/learning makes data-driven and ML-based algorithms considerably capable for dealing with FDI issues as classification problems.  A critical and obvious barrier for a wide and successful application of data-driven and ML-based algorithms to industrial automatic control systems is their (application) explainability and interpretability.

        Biography：Steven X. Ding is a professor of control engineering and the head of the Institute of Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany. He received the Dr.-Ing. (Ph.D) degree in electrical engineering from the University of Duisburg in 1992. Between 1992 and 1994, he was with company Rheinmetall GmbH, Germany. From 1995 to 2001, he was a professor of control engineering at the University of Applied Science Lausitz in Senftenberg, Germany, and served as the vice president of this university during 1998 – 2000. Since 2001, he has been a full-professor of control engineering at the University of Duisburg-Essen. Prof. Ding is an independent researcher. His research interests are model-based and data-driven fault diagnosis, process monitoring and control as well as their applications to automotive and process industry, renewable energy, smart building systems as well as secure cyber-physical systems.

Keynote Speech 02——Professor Biao Huang, Canadian Academy of Engineering Fellow, IEEE Fellow

Transfer Learning and Its Related FDI

Professor Biao Huang, Canadian Academy of Engineering Fellow, IEEE Fellow

Department of Chemical and Materials Engineering, University of Alberta, Canada

        Abstract：With advance in computing power and multisensory technology, massive data accumulated can be processed by data-driven techniques to learn underlying driving forces and/or hidden patterns for better monitoring and control of industrial processes. In order to train a data-driven model with satisfactory performance, sizable amounts of labelled data are required. However, it is often expensive, time consuming and labor-intensive to gather well-labelled data although massive data are being collected. It becomes even more demanding to collect such labelled data for an industrial plant under situations such as, for example, when the plant has just started operation, the labels are the faulty events that are rare events, or the labels are lab data that are sampled sparsely. This is commonly referred to as the cold-start problem, where decisions have to be made while little or almost nothing has been known about the environment. In the context of fault detection and diagnosis (FDI), how to transfer the knowledge learned from some existing processes into a related target process that has limited labels to establish a satisfactory FDI is of great practical interest. This presentation will give a systematic overview of transfer learning and its related fault detection and diagnosis along with its perspective.

        Biography：Biao Huang obtained his Ph.D. degree in Process Control from the University of Alberta, Canada, in 1997. He had an MSc degree (1986) and a BSc degree (1983) in Automatic Control from the Beijing University of Aeronautics and Astronautics. He joined the University of Alberta in 1997 as an Assistant Professor in the Department of Chemical and Materials Engineering and is currently a Full Professor. He held position of NSERC Senior Industrial Research Chair. He is an IEEE Fellow, Fellow of the Canadian Academy of Engineering, Fellow of the Chemical Institute of Canada, and recipient of many awards, including Germany’s Alexander von Humboldt Research Fellowship, APEGA Summit Award in Research Excellence, ASTech Outstanding Achievement in Science and Engineering Award, R.S. Jane Award and the best paper award from the Journal of Process Control. Biao Huang’s research interests include process data analytics, machine learning, system identification, image processing, fault detection and isolation, and soft sensors. He has published five books and over 500 journal papers.  Biao Huang currently serves as the Editor-in-Chief for IFAC Journal Control Engineering Practice, Subject Editor for Journal of the Franklin Institute, Associate Editor for IEEE/CAA Journal of Automatica Sinica, and Associate Editor for Journal of Process Control.

Keynote Speech 03——Professor Donghua Zhou

Research Progress on Anomaly Monitoring for Non-Stationary Industrial Processes

Professor Donghua Zhou

Vice President of Shandong University of Science and Technology, 

Jointly Appointed Professor at Tsinghua University

        Abstract：Non-stationary industrial processes are prevalent, and ensuring their safe and stable operation is very important. Therefore, the anomaly monitoring of such systems has garnered significant attention from the academic community over the past two decades, leading to the development of numerous methodologies. This report provides an overview of the research context and significance of this issue, and summarizes the current state of existing research and major approaches, and highlights some of the key research progress made by our research team in this field. Finally, potential directions for future development are discussed.

        Biography：Dr. Donghua Zhou is a Professor and Doctoral Supervisor. He obtained his Ph.D. from Shanghai Jiao Tong University and conducted postdoctoral research at Zhejiang University. He is currently the Director of the National-Local Joint Engineering Research Center for Mine Safety Detection Technology and Automation Equipment (Qingdao), Vice President of Shandong University of Science and Technology, and Dually Appointed Professor at Tsinghua University. He previously served as the Director of the Department of Automation at Tsinghua University, the Director of the Higher Education Automation Discipline Teaching Steering Committee under the Ministry of Education, and the member of the Discipline Evaluation Group for Control Science and Engineering under the State Council.

He is a recipient of the National Science Fund for Distinguished Young Scholars, the Changjiang Scholar Distinguished Professor, a leading talent in the National "Ten-Thousand Talents Program", the leader of an Innovative Research Group under the National Natural Science Foundation of China, and a recipient of the Special Government Allowance from the State Council. He also holds positions as a member of the IFAC Technical Committee on Technical Processes for Fault Diagnosis and Safety, the Deputy Director of the Chinese Association of Automation and the Director of the Technical Process Fault Diagnosis and Safety Professional Committee. His primary research interests include dynamic system fault diagnosis and fault-tolerant control, as well as operational safety assessment theory. He has received numerous awards, including two National Natural Science Second Prizes, one National Teaching Achievement Second Prize, and four first prizes at provincial and national scientific and technological awards from various academic associations.

Dr. Zhou has been honored with the National Outstanding Postdoctoral Award, the Fok Ying Tung Education Foundation Young Teacher Award, the 6th China Youth Science and Technology Award, the National New Century Talents Project, the National Outstanding Science and Technology Worker, and the National Huang Danian-style Leading Teacher Team Leader. He is the leading talent of the Shandong Province's "Taishan Scholar" Advantageous and Distinctive Discipline Team. Moreover, he is also the Expert of the Taishan Scholar Climbing Program, and has been included in the list of globally highly cited scientists and the top 2% of scientists worldwide. He is also elected as a Fellow of IEEE/AAIA/IET/CAA.