Prognostics and Health Management for Intelligent Transportation Assets
Session Organizers:
Xiaoxi Hu, Tsinghua University, School of Vehicle and Mobility, China
Email: xiaoxihurail@gmail.com
Junyu Qi, Institute of Engineering Mechanics, Karlsruhe Institute of Technology, Germany
Email: junyu.qi@kit.edu
Tianyu Gao, School of Electronics and Information Engineering, Harbin Institute of Technology, China,
Email: gaotianyu0714@hit.edu.cn
Dandan Peng, School of Mechanical Engineering, Northwestern Polytechnical University, China
Email: dandan.peng@nwpu.edu.cn
Zhuyun Chen, School of Electromechanical Engineering, Guangdong University of Technology, China
Email: mezychen@gdut.edu.cn
Transportation assets, including road vehicles, railway systems, aircraft and spacecraft, marine and offshore systems, unmanned aerial vehicles, eVTOLs, and other low-altitude mobility platforms, are essential to modern mobility, logistics, and infrastructure services. These systems usually operate under complex, dynamic, and safety-critical conditions, making them vulnerable to degradation, faults, unexpected failures, and cascading operational risks. Conventional inspection and maintenance strategies based on fixed schedules or reactive actions are increasingly insufficient for intelligent, connected, and highly automated transportation systems.
Recent advances in sensing technologies, signal processing, artificial intelligence, multi-source data fusion, digital twins, and edge computing have created new opportunities for Prognostics and Health Management (PHM) of transportation assets. These technologies enable more effective condition monitoring, fault detection, fault diagnosis, remaining useful life prediction, risk assessment, and maintenance decision-making. However, transportation PHM still faces several challenges, including heterogeneous platforms, complex operating environments, imperfect sensing, limited failure samples, cross-domain variability, real-time deployment requirements, and the need for trustworthy and interpretable decision support.
This special session aims to provide a focused forum for researchers and practitioners working on PHM theories, methods, and applications for intelligent transportation assets. Particular emphasis is placed on reliable health monitoring, robust diagnosis and prognosis, operational situational awareness, and intelligent maintenance management across automotive, railway, aerospace, maritime, offshore, and low-altitude mobility systems.
Contributions are invited on, but not limited to, the following topics:
- Condition monitoring and health state assessment of transportation assets
- Fault detection, fault diagnosis, and fault isolation in safety-critical transportation systems
- Remaining useful life prediction and degradation modeling under uncertain operating conditions
- Multi-sensor, multi-modal, and heterogeneous data fusion for transportation PHM
- Situational awareness, risk assessment, and safety state estimation for intelligent transportation systems
- Digital twin, edge intelligence, and trustworthy AI for predictive maintenance
- Applications and case studies in automotive, railway, aerospace, maritime, offshore, UAV, eVTOL, and low-altitude mobility systems