Advancements in Reliability and Testability of Complex Cyber-Physical Systems

Submission Guideline:

Please submit your manuscript via Online Submission System: https://easychair.org/conferences/?conf=rcae2024
Please choose Special Session: Advancements in Reliability and Testability of Complex Cyber-Physical Systems

Introduction:

The estimation of Remaining Useful Life (RUL) plays a critical role in the design and management of complex systems, such as nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, infrastructure, and manufacturing plants. Coupled with the design of testability (DoT), and fault detection and isolation (FDI), these elements ensure the reliability, maintainability, and safety of such systems. The integration of reinforcement learning techniques and cyber-physical systems further enhances the predictive accuracy and operational efficiency of these complex environments. This special session aims to explore the latest advancements and ongoing challenges in RUL prediction, system reliability, DoT, FDI and reinforcement learning techniques bringing together leading researchers and industry professionals to discuss innovative methodologies and their practical applications in the cyber-physical systems.

Topics of Interest
We invite submissions addressing, but not limited to, the following topics:
 RUL Prediction with Interdependent Degradation Processes
 RUL Prediction under Varying Operating Conditions
 Physics-Informed Degradation Modeling and Prognostics
 Degradation Modeling with Emphasis on Physically Interpretable Weights/Parameters
 System Run-to-Failure Mechanism
 Integration of Degradation Modeling with Prognostic Modeling
 RUL Prediction-Based Decision Models
 Self-Data-Driven Prognostic Approaches
 Design of Testability
 Fault Detection and Isolation
 Reinforcement Learning in Prognostics

Organizers:

Chun Liu, Associate Professor

Shanghai University, China

Chun Liu Shaohua Yangreceived the Ph.D. degree in control theory and engineering from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2020. From 2017 to 2018, he was a Visiting Scholar with the Faculty of Science and Engineering, University of Hull, Hull, U.K. He is currently an Associate Professor with the School of Mechatronic Engineering and Automation, and also with the Institute of Artificial Intelligence, Shanghai University, Shanghai, China. His research interests include fault diagnosis and fault tolerant control for multi-agent systems and their applications.

Cunsong Wang, Associate Professor

Nanjing Tech University, China

Cunsong Wang received the Ph.D. degree in Control Theory and Control Engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2021. From 2019 to 2020, he was a Visiting Scholar with Lassonde School of Engineering, York University, Toronto, Canada. He is currently an Associate Professor with the Institute of Intelligent Manufacturing, Nanjing Tech University, Nanjing, China. His current research interests include data-driven fault prognosis and health management.

Yang Li, Assistant Professor

Shanghai University, China

Yang Li received the B.S. degree in electrical engineering and automation and the M.S. degree in control science and engineering from Qufu Normal University, Rizhao, China, in 2014 and 2017, respectively; and he received the Ph.D. degree in College of Automation from Nanjing University of Aeronautics and Astronautics, China, in 2022. He had ever been a Visiting Research Scholar with the Energy Department, Politecnico di Milano, Italy, in 2019-2021. Now he is an Assistant Professor/Post-Doctoral Fellow with the School of Mechatronic Engineering and Automation, Shanghai University, China. His research interests include fault detection and testability design, accelerated testing based on lifetime analysis.