Special Session
SS 07 - Data-driven fault diagnosis and fault-tolerant control: Recent Advancement
SS 07 - Data-driven fault diagnosis and fault-tolerant control: Recent Advancement
Organizers
Yuchen Jiang, Harbin Institute of Technology, China
and
Yunsong Xu, National University of Defense Technology, China
and
Hao Luo, Harbin Institute of Technology, China
and
Shen Yin, Norwegian University of Science and Technology, Norway
Session Focus
Due to the increasing demands on the safe and reliable operation of complex industrial systems, it has been a research focus to timely detect and diagnose faults/malfunctions, and to equip the systems with fault-tolerance abilities. Data-driven fault diagnosis and fault-tolerant control (FD/FTC) methods have the advantage to take available industrial information and the measurements from sensors as resources for design and implementation. How to make good use of the valuable information from data and how to deal with the problems in the datasets for various types of complex systems significance have been common topics for both academic research and engineering practice. This Special Session is to provide a forum for researchers and industrial engineers to exchange their latest results that contribute to novel techniques, approaches, frameworks, tools, or practical applications, and to discuss the vital issues, challenges and possible future trends which are closely related to data-driven fault diagnosis and fault-tolerant control.
Session Topics
- Data-driven residual generator design
- Multivariate statistical analysis-based and subspace-aided approaches
- Data-driven key performance indicator selection and Plantwide FD/FTC system designs
- Data-driven performance degradation evaluation
- Performance-supervised and performance-oriented FD/FTC approaches
- Explainable artificial intelligence (XAI) for FD/FTC of complex nonlinear systems
- Security issues in data-driven fault diagnosis and fault-tolerant control
- Computer vision-based fault-tolerant control and performance recovery
- Online and real-time implementation with practical applications
- Other topics that are closely related to the scope of the SS
Bio of the organizers
Dr. Yuchen Jiang received his B.E. degree and Ph.D. degree from the Department of Control Science and Engineering, Harbin Institute of Technology, in 2016 and 2021, respectively. Dr. Jiang received the Student Paper Travel Award from the IEEE ICIT 2016. He has published over 20 peer-reviewed journal papers and 20 more at international conferences. His research interests include data-driven safety and security monitoring, fault diagnosis, fault-tolerant control, and the applications to complex systems such as industrial cyber-physical systems.
Dr. Yunsong Xu received the B.E. degree in automation and the M.Sc. degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2012 and 2014, respectively, and the Ph.D. degree in electrical engineering and information technology from the University of Duisburg-Essen, Duisburg, Germany, in 2018. He is currently with National University of Defense Technology, Changsha, China. His research interests include fault-tolerant control, reinforcement learning, vision-based control systems and cyber-physical systems.
Prof. Hao Luo received the B.E. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 2007, and the M.Sc. and Ph.D. degrees in electrical engineering and information technology from the University of Duisburg-Essen, Duisburg, Germany, in 2012 and 2016, respectively. He is currently a Full Professor with the School of Astronautics, Harbin Institute of Technology, Harbin, China. His research interests include model-based and data-driven fault diagnosis, fault-tolerant systems, and their plug-and-play application on industrial systems.
Prof. Shen Yin received the M.Sc. and Ph.D. (Dr.-Ing.) degree from the University of Duisburg-Essen, Germany. He is currently DNV-GL Professor with the Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology. His research interests include safety, reliability of complicated systems, system and control theory, data-driven and machine learning approaches, applications in large-scale systems and industrial cyber-physical systems.