AI-Driven Intelligent Fault Diagnosis, Tolerant control and Predictive Maintenance for Autonomous Systems

Introduction: The increasing complexity and autonomy of modern systems, including autonomous vehicles, unmanned aerial vehicles (UAVs), and intelligent transportation platforms, pose significant challenges to their safe and reliable operation. Traditional maintenance and control strategies, which depend on scheduled inspections and reactive repairs, are insufficient for meeting the stringent safety and availability requirements of these systems. Artificial intelligence and data-driven approaches provide powerful tools for transforming how faults are detected, tolerated, and prevented in autonomous systems.
This special session aims to bring together researchers, engineers, and practitioners to present and discuss the latest advances in AI-driven fault diagnosis, fault-tolerant control, and predictive maintenance for autonomous systems. The scope encompasses: (1) intelligent fault detection and diagnosis methods using machine learning, deep learning, and transfer learning; (2) fault-tolerant control strategies that enable systems to maintain acceptable performance under component failures or degraded conditions; (3) predictive maintenance frameworks based on multi-source sensor data fusion, digital twin technologies, and remaining useful life estimation; (4) knowledge representation, reasoning, and experience-driven decision support for automated fault analysis; and (5) integrated health management architectures combining diagnosis, tolerant control, and maintenance planning for autonomous vehicles, UAVs, and multi-agent systems. By fostering interdisciplinary discussions across fault diagnosis, control theory, and AI communities, this session seeks to bridge the gap between theoretical research and practical deployment, ultimately contributing to safer, more resilient, and self-healing autonomous systems.

Organizers:

	Li Guo, Anhui Polytechnic University, China Li Guo is a Professor at the School of Electrical Engineering, Anhui Polytechnic University (AHPU), and serves as the Vice Dean. She received her Ph.D. in Communication and Information System from Sichuan University in 2013. From 2016 to 2017, she was a visiting scholar at the University of Groningen, the Netherlands, supported by the China Scholarship Council. Her research group is supported by the Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education. She has published over 100 papers in leading international journals and conferences with more than 50 SCI-indexed publications, including a Best Paper Award at RCAE 2024 and Young Scientist Award at RCAE 2025. Her current research focuses on AI-driven intelligent systems and data-driven fault diagnosis for autonomous vehicles and unmanned aerial vehicles. She is an IEEE/CAA/CAAI Member.
	Yuan Ge, Anhui Polytechnic University, China Yuan Ge is a Professor at the School of Electrical Engineering, AHPU. He received his Ph.D. from the University of Science and Technology of China. He was a visiting scholar at Tsinghua University. His research focuses on networked system analysis and control, complex network information security, and energy internet. He has led over 10 national and provincial projects including NSFC grants, and published over 50 papers with 20+ SCI/EI indexed.
	Yuhang Xu, Nanjing University of Aeronautics and Astronautics, China Yuhang Xu received her Ph.D. in automatic control from NUAA in 2022. During 2019-2021, she was a visiting Ph.D. student at the KIOS Research and Innovation Center of Excellence, University of Cyprus. She is currently an Associate Researcher at NUAA. Her research interests include fault-tolerant control and game control for multi-agent systems.
	Xiaolu Chen, Anhui Polytechnic University, China Xiaolu Chen is a Lecturer at the School of Electrical Engineering, AHPU. She received her Ph.D. in Control Science and Engineering from Beijing University of Chemical Technology in 2021 and completed postdoctoral research at Peking University (2021-2024). Her research focuses on complex process modeling, fault diagnosis, and root cause analysis. She has published over 20 high-level papers.

Submission Guideline:

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