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please choose Special Session: Intelligent Sensing and Fault Diagnosis of Mechanical Equipment
Jerome Antoni, University of Lyon, France
Jerome Antoni currently holds a full professor position with the University of Lyon, France. His current research interests include the development of signal processing methods in mechanical applications. Professor Antoni served as a Handling Editor for the International Journal of Condition Monitoring, the International Journal of Rotating Machinery, and Diagnostika. He is currently with the Editorial Board of Mechanical Systems and Signal Processing and Applied Sciences.
Dong Zhen, Hebei University of Technology, China
Dong Zhen is currently a professor in the School of Mechanical Engineering of Hebei University of Technology, and the supervisor of doctoral students. He is a special expert under the "Hundred Talents Program" of Hebei Province. He is mainly engaged in the research of mechanical system fault diagnosis and condition monitoring, intelligent detection of mechanical system, vibration noise signal processing, mechanical fault feature extraction and pattern recognition, wind turbine fault diagnosis and condition monitoring technology.
Changqing Shen, Soochow University, China
Changqing Shen is currently a professor and supervisor of PhD and Master's degree students in the School of Railway and Transportation of Soochow University. He has been selected to be the mobile supervisor of the National Natural Science Foundation of China in 2021, as well as the high-level talent of "Six Talent Summits" in Jiangsu Province. His research interests include signal processing, condition monitoring and intelligent diagnosis of key components of transportation systems.
Ran Wang, Shanghai Maritime University, China
Ran Wang is currently an associate professor, supervisor of doctoral students, and deputy director of the Department of Mechanics at the college of Logistics Engineering, Shanghai Maritime University. She received her Doctor of Engineering degree in Mechanical Design and Theory from the State Key Laboratory of Mechanical Systems and Vibration, Shanghai Jiao Tong University in 2014. In recent years, she has hosted and participated more than 10 research projects, including the project of National Natural Science Foundation of China. Currently, she is working on 1 project of Shanghai Natural Science Foundation, 2 projects of State Key Laboratory, and 3 projects of enterprises and institutions. She has published more than 40 papers in authoritative academic journals, including more than 30 SCI/EI retrieved papers as the first/corresponding author. Her research interests include artificial intelligence-driven machinery fault diagnosis, acoustic array intelligent sensing, signal processing and application, and ultrasonic nondestructive testing technology. She is currently serving as an expert of the Shanghai Municipal Science and Technology Commission, a director of Shanghai Modern Design Research Association, and a member of the Chinese Society of Vibration Engineering.
Tianyang Wang, Tsinghua University, China
Tianyang Wang is currently an associate professor at the Department of Mechanical Engineering, Tsinghua University. He is mainly engaged in the research work in the field of fault diagnosis and signal analysis of large-scale rotating machinery, such as wind turbines and aero-engines.
Xuefang Xu, Yanshan University, China
Xuefang Xu is currently a lecturer in the School of Electrical Enginieering, Yanshan University. His research interests include intelligent fault diagnosis, computational fluid dynamics analysis and time-series prediction based on machine learning.
With the advent of Industry 4.0, the intelligentization, automation, and efficiency of mechanical equipment have become an irreversible trend. However, various faults inevitably occur during the operation of mechanical equipment, which not only affects production efficiency but also poses significant economic losses and safety risks. Therefore, how to achieve intelligent perception and fault diagnosis of mechanical equipment, improving equipment reliability, safety, and maintenance efficiency, has become a common focus of both the industrial and academic communities. This special session aims to focus on the latest research findings and technological advances in the field of mechanical fault diagnosis and to discuss the application scenarios and future development trends of intelligent sensing technology in fault diagnosis.
Topics of interest for the Special Session include, but are not limited to:
Failure mechanism and feature extraction methods of mechanical equipment;
AI-driven device intelligent fault diagnosis methods;
Multi-physical field intelligent perception and information fusion in fault diagnosis techniques;
Acoustic-based fault diagnosis technologies;
Digital twin-driven mechanical fault diagnosis methods;
Engineering applications of machinery fault diagnosis.
随着工业4.0的出现，机械设备作为工业生产的核心组成部分的智能化、自动化和效率已成为一种不可逆转的趋势。然而，机械设备在运行过程中不可避免地会发生各种故障，这不仅影响了生产效率，也造成了重大的经济损失和安全风险。因此，如何实现对机械设备的智能感知和故障诊断，提高设备的可靠性、安全性和维护效率，已成为工业界和学术界的共同关注的焦点。本专题旨在重点介绍机械故障诊断领域的最新研究成果和技术进展，并讨论智能传感技术在故障诊断中的应用场景和未来的发展趋势。
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