Special Session XIV

Current Status and Challenges of the Integrated Development of Maglev Transportation Systems and Intelligent Monitoring

(磁悬浮运输系统与智能监测融合发展的现状与挑战)



Chair:

Co-chair:

Jun Zheng

Zhiqiang Long

Southwest Jiaotong University, China

National University of Defense Technology, China


Keywords:

Topics:

· Electromagnetic Levitation (电磁悬浮)

· Superconducting Levitation (超导悬浮)

· Linear Motor (直线电机)

· Electromechanical Coupling (机电耦合)

· Intelligent Monitoring (智能监测)

· Fault Prediction (故障预测)

· Maglev Transportation (磁悬浮运输)

· Linear Motor (直线电机)

· Electromechanical Coupling Dynamics (机电耦合动力学)

· Condition Monitoring and Data Fusion for Maglev Systems (磁悬浮系统状态监测与数据融合)

· Fault Diagnosis and Health Management for Maglev Systems (磁悬浮系统故障诊断与健康管理)

· Failure Models, Identification and Control of Key Components in Maglev Systems (磁悬浮关键部件失效模型、识别与控制)

· Application of Artificial Intelligence in Maglev Transportation Systems (人工智能在磁悬浮运输系统中的应用)


Summary:

· With the continuous development of maglev technology, the operating environment and system structure are becoming increasingly complex, putting forward higher requirements for high reliability and safe operation. This special session focuses on the research progress of different maglev systems and the cutting-edge issues of intelligent monitoring, fault prediction, and data fusion within maglev fields. It explores how to use artificial intelligence, machine learning, and big data analysis methods to achieve high-precision perception of the operating state, prediction of fault trends, and fault-tolerant control. This session will invite experts and scholars from the fields of scientific research, universities, and engineering practices to share the latest research findings and application cases, exchange views on the current technical bottlenecks and development challenges, and promote the maglev system to move towards intelligence and autonomy.


· 随着磁悬浮技术的不断发展，其运行环境和系统结构日益复杂，对高可靠性与安全运行提出了更高要求。本专题聚焦不同磁悬浮制式研究进展及其中的智能监测、故障预测与数据融合的前沿问题，探讨如何借助人工智能、机器学习、大数据分析手段，实现对磁悬浮运行状态的高精度感知、故障趋势预测与容错控制。专题将邀请来自科研、高校及工程实践领域的专家学者，分享最新研究成果与应用案例，交流当前面临的技术瓶颈与发展挑战，推动磁悬浮系统向智能化、自主化方向迈进。


Submission Deadline: 2025.8.31