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Zongjie Zhou |
15099320514 |
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Shaohong Wang |
13601122562 |
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Jie Sun |
18911813143 |
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Zhifei Shu |
18809429553 |
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Shiyu Dou |
19199706731 |
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Jiadong Hua |
18810811398 |
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Jinyang Jiao |
18811608675 |
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Jianpeng Wu |
15811319103 |
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Yue Song |
13811108916 |
Special Session #01 Advanced signal processing enabled intelligent fault diagnosis and measurement
Special Session #03 Remaining Useful Life (RUL) Prediction of Rotating Machinery
Special Session #04 Statistical Signal Processing and Measurement
Special Session #06 The reliability of intelligent equipment
Special Session #07 Belief reliability theory and its application in smart operation and maintenance
Special Session #10 Reliability of New Energy Storage System
Special Session #14 Artificial self-recovery of high-end mechanical equipment
Special Session #15 Safety, Reliability and Intelligent Operation and Maintenance Technology for
Special Session #16 Physics-inspired Multi-modal Intelligent Fault Diagnosis of Machinery
Special Session #21 Key Technologies for the Operation and Maintenance of Industrial Robots
Special Session #22 System dynamics, control and diagnosis of key components of rail vehicle
Advanced signal processing enabled intelligent fault diagnosis and measurement
Session Organizers:
Tianfu Li, Kunming University of Science and Technology
Tianfu Li, is currently a Distinguished Professor in Faculty of Mechanical and Electrical Engineering at Kunming University of Science and Technology, where he received the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University in 2024. From Oct. 2022 to Oct. 2023, he was a visiting scholar with the Laboratory of Intelligent Maintenance and Operations Systems, EPFL, Switzerland. His current research is focused on graph representation learning, explainable artificial intelligence, and intelligent maintenance. His honors and awards include the IEEE Instrumentation and Measurement Society Outstanding Reviewers in 2022 and 2023, and Andrew P. Sage Best Transactions Paper Award for IEEE TSMC. He currently serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement.
Email: tianfu.li@kust.edu.cn
Yu Guo, Kunming University of Science and Technology
Yu Guo is a full professor at Kunming University of Science and Technology, where he received the Ph.D. degree in mechanical engineering from Chongqing University in 2003. From Sep. 2007 to Aug. 2008, he was a visiting scholar at the National University of Singapore, and from Jul. 2017 to Jan. 2018, he was also a visiting scholar at the University of Michigan. His research interests include mechanical dynamic testing and measurement technology, vibration analysis, and other fields. Dr. Yu currently serves on the editorial boards of some Chinese journals, including the Journal of Vibration Engineering, the Journal of Vibration and Shock, and the Journal of Vibration, Measurement & Diagnosis.
Email: guoyu@kust.edu.cn
Xiaoqin Liu, Kunming University of Science and Technology
Xiaoqin Liu is a Full professor at the Faculty of Mechanical and Electrical Engineering at Kunming University of Science and Technology, China. His research interests including dynamical measurement and signal processing for machinery fault diagnosis, sound source identification and vibration control. Dr. Liu is the director of the Intelligent Maintenance Research Center for Advanced Equipment of Yunnan Province, the Chief Expert of Shanghai Huayang Measurement Corp. Dr. Liu has led several research projects in recent years, including National Science Foundation projects and R&D projects from the industry, and have published more than 50 research articles.
Email: liuxiaoqin@kust.edu.cn
Download: Special Session #1.pdf
Signal processing technology can realize customized analysis of machine monitoring signals, thereby can achieve accurate fault diagnosis results. Its solid theoretical foundation and excellent interpretability enable it to maintain long-term vitality in the field of Prognostics and Health Management (PHM). However, with the continuous advancement of industrialization, the operating conditions of machines have become more and more complex, which has aggravated the difficulty the feature extraction of monitoring signals, thus bringing huge challenges to traditional signal processing technology. Therefore, it is urgent to study advanced signal processing technology to realize feature mining of measurement signals, and at the same time combine it with artificial intelligence to construct an interpretable intelligent fault diagnosis model to meet the needs of the industrial big data era.
Suitable topics for this special session include but are not limited to:
Advanced sensing and measurement technology in high-end equipment and infrastructure
Advanced signal processing for fault diagnosis and measurement
Advanced signal processing for feature extraction and intelligent fault diagnosis
Design, control and sensor measurement of complex electromechanical systems (such as robots
and aircraft engines)
Signal processing-informed intelligent fault diagnosis
Interpretable intelligent fault diagnosis for PHM and measurement
Intelligent fault detection under complex time-varying operating conditions
Next-Generation Condition Monitoring: The Role of Digital Twins in Predictive Maintenance
Session Organizers:
Prof. Ke Feng, Xi’an Jiaotong University;
Prof. Jiawei Xiang, Dr. Yi Liu, Wenzhou University
Download: Special Session #2.pdf
The rapid advancement of Digital Twin technologies is reshaping how industries approach condition monitoring, predictive maintenance, and prognostics for complex systems. Digital twins, which create dynamic virtual replicas of physical assets, enable real-time monitoring and provide deep insights into the health and performance of industrial machinery, such as motors, turbines, bearings, and other rotating systems. By leveraging IoT sensors, Big Data, and machine learning, digital twins offer predictive capabilities that can minimize downtime, reduce maintenance costs, and significantly enhance overall efficiency.
This special session aims to showcase the transformative potential of digital twins in condition monitoring and predictive maintenance across industries. It will explore the latest research, methodologies, and real-world applications of digital twin technologies to optimize machinery health, enhance decision-making, and improve operational performance.
We invite contributions from researchers, and industry professionals to share their expertise and experiences in using digital twins for real-time asset health monitoring, fault detection, and lifecycle management.
Topics of Interest:
Digital Twin Frameworks and Architecture for Condition Monitoring
Predictive Maintenance and Fault Diagnosis Using Digital Twins
Anomaly Detection and Condition Monitoring
Machine Learning and Artificial Intelligence in Digital Twin Technologies
Lifecycle Management and Optimization with Digital Twins
Digital Twin Applications in Industry 4.0
Reliability Engineering with Digital Twins
Any other related topics
Remaining Useful Life (RUL) Prediction of Rotating Machinery
Session Organizers:
Prof. Liuyang Song
Dr Liuyang Song received the Ph.D. degree from Mie University, Tsu, Japan, in 2017. She is currently a professor with the College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China. Her current research interests include signal processing, intelligent fault detection and RUL prediction.
Email: xq_0703@163.com
Assoc. Prof. Naipeng Li
Dr. Naipeng Li received the Ph.D. degree from Xi’an Jiaotong University, P. R. China. He was a visiting scholar at Georgia Institute of Technology, US. He is currently an associate professor in the Mechanical Engineering School, Xi’an Jiaotong University, Xi’an, P. R. China. His research interests include condition monitoring, intelligent fault diagnostics and RUL prediction of mechanical systems.
Email: naipengli@mail.xjtu.edu.cn
Assoc. Prof. Jianpeng Wu
Dr. Jianpeng Wu received the Ph.D. degree from Beijing Institute of Technology, P. R. China. He was a visiting scholar at Rice University, US. He is currently an associate professor in the Mechanical Engineering School, Beijing Information Science and Technology University, Beijing, P. R. China. His research interests include vehicle transmission system digital twin, and RUL prediction of mechanical systems and key components.
Email: 15811319103@163.com
Download: Special Session #3.pdf
The remaining useful life (RUL) prediction has attracted substantial attention recently due to its importance for the Prognostic and Health Management. The focus of this special session is on RUL prediction methods for rotating machinery. It aims to provide a platform for experts and scholars to engage in discussions on advanced technologies and their applications in RUL prediction for rotating machinery. Submissions related to advanced and emerging technologies, as well as their practical applications in RUL prediction studies are encouraged.
The topics of interest include, but are not limited to:
Methodologies for RUL prediction based on machine learning
RUL prediction approaches utilizing mathematical and physical models
Multiple data-driven strategies for RUL prediction
Approaches for constructing health indicators (HIs)
Methods for selecting the first prediction time (FPT)
Applications of RUL prediction in railway systems, aerospace systems and wind energy system
Real-time technologies for RUL prediction
Other innovative technologies for RUL prediction
Statistical Signal Processing and Measurement
Session Organizers:
Prof. Wei Fan
Dr. Wei Fan received her Ph.D. degree from the City University of Hong Kong, Hong Kong, in 2018. She is currently a professor in the School of Mechanical Engineering at Jiangsu University, Zhenjiang, China. Her research interests include statistical signal processing and non-contact measurement.
Email: weifan@ujs.edu.cn
Assoc. Prof. Laihao Yang
Dr. Laihao Yang received his Ph.D. degree from Xi’an Jiaotong University, China, in 2019. He is currently an associate professor in the School of Mechanical Engineering at Xi’an Jiaotong University, Xi’an, China. His research interests include AI for science and intelligent sensors.
Email: yanglaihao@xjtu.edu.cn
Prof. Wilson Wang
Dr. Wilson Wang received his Ph.D. degree from the University of Waterloo, Canada, in 2002. He is currently a professor in the Department of Mechanical Engineering, cross-appointed by the Department of Electrical and Computer Engineering, at Lakehead University, Canada. His research interests include AI and machine learning, smart systems, and instrumentation.
Email: wilson.wang@lakeheadu.ca
Dr. Zhenling Mo
Dr. Zhenling Mo received his Ph.D. degree from the School of Data Science, City University of Hong Kong, Hong Kong, in 2024. He is currently a postdoctoral researcher in the Department of Data Science, College of Computing, City University of Hong Kong, Hong Kong. His research interests include causal machine learning, intelligent model generalization, mechanical signal processing, fault diagnosis, and prognosis.
Email: zhenling.mo@my.cityu.edu.hk
Download: Special Session #4.pdf
Statistical signal processing and measurement are vital for advancing intelligent systems and industrial applications, enabling precise data interpretation, decision-making, and system optimization. This special session focuses on providing a platform for experts and researchers to discuss the latest advancements and applications in these fields. Submissions addressing emerging methodologies, theoretical developments, and practical applications in statistical signal processing and measurement are highly encouraged.
The topics of interest include, but are not limited to:
Signal detection, estimation, and classification
Intelligent information processing
Intelligent model generalization
Statistical signal processing
Non-contact measurement
Industrial applications of signal processing techniques
Condition monitoring, fault diagnosis and intelligent maintenance of mechanical equipment
Session Organizers:
Professor Ling Xiang
Ling Xiang, Ph.D., professor in the School of Mechanical Engineering, North China Electric Power University, Hebei, P. R. China. Dr. Ling Xiang received the Ph.D. degree from North China Electric Power University, China. She was also a visiting scholar at University of Southampton, UK. Her research interests include condition monitoring, fault diagnosis, intelligent maintenance, remaining life prediction for rotating machine. She is now focusing on intelligent fault diagnosis and running maintenance of wind energy equipment.
E-mail: xiangling@ncepu.edu.cn
Professor Rujiang Hao
Rujiang Hao, Ph.D., professor in the Mechanical Engineering School, Shijiazhuang Tiedao University, Hebei, P. R. China. Dr. Rujiang Hao received the Ph.D. degree from Tsinghua University, China. He was a visiting scholar at Cardiff University, UK. His research interests include condition monitoring, intelligent fault diagnostics and RUL prediction of mechanical systems.
Email: haorj@stdu.edu.cn.
Professor Dong Zhen
Dong Zhen, Ph.D., professor in the School of Mechanical Engineering, Hebei University of Technology, Tianjin, P. R. China. Dr. Dong Zhen received the Ph.D. degree from University of Huddersfield, UK. He was also a visiting scholar at University of Huddersfield, UK. His research interests include condition monitoring and fault diagnosis of advanced equipment, vibration and acoustic signal processing, pattern recognition.
Email: d.zhen@hebut.edu.cn
Download: Special Session #5.pdf
The development of intelligence is that the condition monitoring and intelligent operation and maintenance of mechanical equipment are increasingly concerned. Researching effective methods for monitoring and diagnosing, comprehensively improving the operational safety and stability of mechanical equipment, improving the quality and efficiency of mechanical equipment, is an important trend in line with the current development of mechanical systems. In this section, we aim to provide a forum for colleagues to gather and propagate the most recent research results and breakthroughs in condition monitoring and fault diagnosis of mechanical equipment, including the application of numerous theories and technologies such as dynamics modeling, sensor layout, data acquisition, parameter measurement, signal analysis, feature extraction, fault diagnosis, anomaly detection, structural damage identification, early warning, health condition assessment, residual life prediction, and other related software even hardware technology.
The topics of interest include, but are not limited to:
Dynamic modeling and simulation techniques for mechanical equipment
Mechanical equipment condition monitoring and fault diagnosis.
Data acquisition and processing algorithms for mechanical equipment fault diagnosis.
Anomaly detection and early warning for mechanical system.
Novel diagnosis techniques and measurement systems.
Health condition assessment and intelligent maintenance of mechanical equipment.
The reliability of intelligent equipment
Session Organizers:
Assoc. Prof. Jie Liu
Dr. Jie Liu received his Ph.D. degree from CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France, in 2015. He is currently an associate professor with the School of Reliability and Systems Engineering at Beihang University, Beijing, China. His research interests include prognostics and health management, machine learning, causal inference, etc.
Email: liujie805@buaa.edu.cn
Prof. Yiping Fang
Dr. Yiping Fang received his Ph.D. degree from École Centrale Paris, Gif-sur-Yvette, France, in 2015. He is currently a professor at Industrial Engineering Laboratory, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France. He is also a member of the industrial chair Risk and Resilience of Complex Systems. His research interests focus on interdependency modeling, uncertainty quantification, mathematical optimization, advanced computational methods for risk, vulnerability and resilience analysis of critical infrastructures.
Email: yiping.fang@centralesupelec.fr
Assoc. Prof. Dan Xu
Dr. Dan Xu received her B.S. and Ph.D. degrees in School of Mechanical Engineering and Automation from Beihang University, Beijing, China, in 2003 and 2009, respectively. She is an Associate Professor in School of Reliability and Systems Engineering, Beihang University. Her primary research interests include reliability design and assessment, intelligent fault diagnosis and life prediction.
Download: Special Session #6.pdf
The development and integration of Artificial Intelligence (AI) have catalyzed significant advancement in both society and industry. The field of reliability engineering has also been significantly improved by AI-driven techniques, including fault diagnosis, failure prediction, and maintenance optimization. As AI systems continue to evolve and become increasingly integral to mission-critical applications, ensuring their reliability has become a crucial priority. New frameworks, methodologies, and tools are needed to address the unique characteristics and challenges presented by these systems. This session aims to provide a platform for experts to present and discuss cutting-edge research on the dual perspectives of “AI for Reliability” and “Reliability of AI”, and explore innovative solutions to reliability challenges in AI-powered systems.
The topics of interest include, but are not limited to：
Methods and approaches on integrating AI into reliability engineering
Predictive maintenance and fault diagnosis using AI
AI-driven decision-making and optimization for system reliability
Uncertainty quantification, propagation and risk management in AI-powered systems
Reliability modeling, assessment and evaluation of AI algorithms and systems
Improving and enhancing the reliability of AI algorithms and systems
Reliability studies of AI-powered systems: unmanned aerial vehicles, autonomous vehicles, autonomous ships, robots, and beyond
Belief reliability theory and its application in smart operation and maintenance
Session Organizers:
Associate Research Fellow Yang Hu
Dr. Yang Hu received his Ph.D. from Politecnico di Milano, Italy in 2015. He is currently an associate research fellow with the Smart Aviation Center, Hangzhou International Innovation Institute, Beihang University, Hangzhou, China. His research interests include prognostics and health management (PHM), artificial intelligence, and modeling & simulation of complex engineering systems.
Email: yang_hu@buaa.edu.cn
Assoc. Prof. Qingyuan Zhang
Dr. Qingyuan Zhang received his Ph.D. degree from Beihang University, Beijing, China, in 2020. He is currently an associate professor with the Smart Aviation Center, Hangzhou International Innovation Institute, Beihang University, Hangzhou, China. His research interests include belief reliability theory, uncertainty analysis, aviation safety, etc.
Email: zhangqingyuan@buaa.edu.cn
Assoc. Prof. Tianpei Zu
Dr. Tianpei Zu received her Ph.D. degree from Beihang University, Beijing, China, in 2021. She is currently an associate professor with the Smart Aviation Center, Hangzhou International Innovation Institute, Beihang University, Hangzhou, China. Her research interests include belief reliability theory, multi-information fusion, uncertainty quantification, maintenance optimization, etc.
Email: zutp_93@buaa.edu.cn
Download: Special Session #7.pdf
Belief reliability theory is a new reliability theory emphasizing the importance of physical models of products. By formulating multidisciplinary equations, degradation equations, margin equations, and measurement equations, the belief reliability can provide a model-based view for smart operation and maintenance (O&M) under multiple uncertainties. For different kinds of systems or equipment, there need to be various belief reliability modeling, analysis, and optimization methods to empower their O&M processes. Some basic problems in O&M, such as uncertainty quantification and model-based health management, also need more concentration. In this regard, this session aims to provide a platform for experts to present and discuss new research on the belief reliability theory and its application in smart O&M, and explore some generic methods for model-based O&M.
The topics of interest include, but are not limited to:
Belief reliability modeling and analysis methods for smart O&M
Belief reliability centered O&M optimization
Multiple uncertainty quantification and analysis in smart O&M
Model-based prognostics and health management using belief reliability
AI and large model-based methods for belief reliability modeling and smart O&M
Belief reliability enhanced maintenance optimization and management
New methods for model-based smart O&M
New perspective of intelligent detection, diagnosis, prognosis, and maintenance: Generative AI and Industrial Large Model
Session Organizers:
Assoc. Prof. Zhibin Zhao
Dr. Zhibin Zhao received the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, China, in 2020. He then joined the School of Mechanical Engineering, Xi’an Jiaotong University, where he is currently an Associate Professor. In 2019, he was a Visiting Research at the centre of health informatics, The University of Manchester, UK. His research interests include sparse signal processing and machine learning, especially deep learning for machine fault detection, diagnosis, and prognosis.
Email: zhaozhibin@xjtu.edu.cn
Assoc. Prof. Jinyang Jiao
Dr. Jinyang Jiao received the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, P.R. China. He was a visiting scholar with the Department of Mechanical and Industrial Engineering, University of Toronto in 2019. He is currently an Associate Professor with the School of Reliability and Systems Engineering, Beihang University, Beijing, China. His research interests include transfer learning, data analytics, large models, and machinery condition monitoring and intelligent fault diagnosis.
Email: jiaojinyang@buaa.edu.cn
Download: Special Session #8.pdf
The rapid advancements in Generative AI and Industrial Large Models have opened a new frontier in prognosis and health management. These cutting-edge technologies provide innovative methods to address critical challenges in predictive maintenance, anomaly detection, fault diagnosis, and system optimization. By integrating generative capabilities, domain-specific large models, and large Language Model (LLM), industrial applications can achieve unprecedented levels of efficiency, reliability, and scalability. This Special Session aims to bring together researchers and experts to explore state-of-the-art methods, share insights, and present breakthrough innovations.
The topics of interest include, but are not limited to:
New methodologies for generative AI and industrial large model
Methods related to Generative AI, such as GAN, Flow models, and Diffusion models
Key techniques related to LLM, such as transformer structure, learning tasks, and new datasets
Domain-specific pre-training and fine-tuning techniques
Transfer learning using generative AI and industrial large model
Interpretability in generative AI and industrial large model
Uncertainty quantification using generative AI and industrial large model
Multi-task learning using industrial large model
Multi-modality sensor data fusion using industrial large model
Cross-industry collaboration for unified large model frameworks
Review and perspective of Generative AI and LLM for industrial applications
Applications of generative AI and industrial large model for aerospace, rail transport, energy, etc.
AI Methods and Applications in Condition Monitoring, Diagnosis, and Maintenance of Structure, Power, Equipment, etc
Session Organizers:
Prof. Yongbin Liu, Anhui University, China
Email: ybliu@ahu.edu.cn
Prof. Siliang Lu, Anhui University, China
Email: silianglu@ahu.edu.cn
Dr. Hang Wang, Anhui University, China, China
Email: hangwang@ahu.edu.cn
Download: Special Session #9.pdf
The condition monitoring, diagnosis and maintenance of structure, power, equipment etc. can improve the reliability of machine avoiding catastrophic accidents and promote the development of industry. Intelligent and data-driven condition monitoring and fault diagnosis of industry machinery has attracted more attention, which does not require strict mathematical and physical models. In recent years, many advanced artificial intelligence techniques have been developed rapidly, which also promote the progress of equipment intelligent maintenance and operation. This special session focuses on the methods and applications of AI in condition monitoring, diagnosis and maintenance of structure, power, equipment etc., shares new ideas and achievements, and discusses the current challenges and possible solutions in these fields with relevant experts, scholars, and engineers around the world.
The topics of interest include, but are not limited to:
Health condition assessment, fault diagnosis and intelligent maintenance of structure, power, equipment
Novel deep learning tools and data-driven techniques
Advanced edge computing approaches for equipment intelligent maintenance and operation
Intelligent anomaly detection and early warning for various industrial equipment
Advanced sensing technology and its application for renewable energy batteries
Application of advanced AI technology in structural diagnosis system, power system, and industrial equipment
Reliability of New Energy Storage System
Session Organizers:
Assoc. Prof. Cheng Qian
Dr. Cheng Qian is currently working as an Associate Professor in the School of Reliability and Systems Engineering, Beihang University. His research interests include reliability simulation, system reliability design and optimization, reliability digital twin, etc. He is also a committee member in the reliability branch of Chinese Institute of Electronics, and the digital experiment and testing branch of China Simulation Federation.
Email: cqian@buaa.edu.cn
Prof. Bingxiang Sun
Dr. Bingxiang Sun is currently working as a tenured professor in Beijing Jiaotong University, and also the chief expert on energy storage system (SSE) of Key Laboratory of Transport Equipment Multi-Source Power System, Ministry of Education of China. She served as the member in both the Electric Vehicle Committee and the Electric Vehicle Charging/Swapping System and Testing Committee of China Electrotechnical Society, as well as a managing director in Power Battery Technology subcommittee and Electric Vehicle & Energy Transportation System Integration Technology subcommittee, IEEE PES. She mainly focuses on the pack application research for rechargeable battery in transportation, power grid and special fields. She was awarded the first prize of China Machinery Industry Science and Technology Award (2019) and the second prize of Beijing Science and Technology Progress Award (2022). Her research interests include battery modeling and virtual simulation, state estimation (SOC/SOE/SOP/ SOH), low temperature heating and fast charging techniques, degradation prognosis and life assessment, failure detection and warning, optimization management and control.
Assoc. Prof. Quan Xia
Dr. Quan Xia is currently an associate professor with the School of Reliability and Systems Engineering at Beihang University, Beijing, China. His research interests include physics of failure, reliability simulation, AI for reliability design, reliability digital twin, etc. He is a committee member in the digital experiment and testing branch of China Simulation Federation, and currently serves as young editorial board member of the Chinese scientific and technological core journal "Equipment Environmental Engineering".
Email: quanxia@buaa.edu.cn
Download: Special Session #10.pdf
Under the background of double carbon, the rapid growth of renewable energy (i.e. wind and solar), Electric Vehicles and special equipment has promoted the considerable development of new energy storage systems such as the battery energy storage system (BESS), super capacitor (SC), flywheel energy storage system (FESS), and Hydrogen energy storage system (HESS). Reliability and state assessment for these new energy storage systems are key steps in ensuring system safety, improving performance, reducing costs, and supporting intelligent operation and maintenance. They contribute to achieve reliable, efficient, and sustainable energy storage systems, promote the widespread application of clean energy, and achieve energy transformation. However, the multifarious and uncertain user demands increase their complexity of degradation tendencies and failure, and pose a challenge to high-precision life and reliability assessment. To fix the above-mentioned issues, theoretical and applied researches on advanced algorithms, models and methods of reliability and state assessment technologies for new energy storage systems are needed and particularly welcome to be presented in this special session.
Suitable topics include but are not limited to:
Degradation and failure mechanism, life extension measures and reliability of core components in new energy storage systems
Advanced sensing, and fault diagnosis technologies for new energy storage systems based on big data, machine learning methods.
Intelligent predictive maintenance on new energy storage systems, and their core components
AI technologies for state assessment of new energy storage systems, and their core components
Multi-physics and multi-scale simulation technologies for new energy storage systems, and their core components
Battery charging strategy, balancing strategy and battery thermal management
Battery pack optimization configuration, Energy management and optimal control of new energy storage systems, and their core components.
Thermal runaway evolution process, safety forecast, protection methods and anti-spread measures for batteries
Power conversion circuit and device topology design and reliability.
Reliability design and optimization for new energy storage systems, and their core components
Safety and risk management for new energy storage systems, and their core components
Policy and economic analysis for new energy storage systems.
New Developments in Guided Waves (GW) and Nondestructive Testing (NDT): Propagation, Design and Applications
Session Organizers:
Assoc. Prof. Jiadong Hua
Dr. Jiadong Hua received the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, P.R. China. He was a visiting scholar with the Department of Electrical and Computer Engineering, Georgia Institute of Technology between 2015-2016. He is currently an Associate Professor with the School of Reliability and Systems Engineering, Beihang University, Beijing, China. His research interests include Nondestructive testing, Structural health monitoring, Elastic wave metamaterials, and Intelligent ultrasonic detection.
Email: huajiadong@buaa.edu.cn
Assoc. Prof. Fei Gao
Dr. Fei Gao received the Ph.D. degree in mechanical engineering from Xi’an Jiaotong University, P.R. China. He was a visiting scholar with the Department of Mechanical and Aerospace Engineering, University of California, Los Angeles (UCLA) between 2017-2018. He is currently an Associate Professor with the School of Reliability and Systems Engineering, Beihang University, Beijing, China. His research interests include Elastic wave propagation, Structural health monitoring, and Nondestructive detection.
Email: youfeigao@buaa.edu.cn
Download: Special Session #11.pdf
Guided waves in thin-walled structures have generated growing interest in nondestructive testing field over past decades due to their long-distance propagation capacity, cost-effective actuating and sensing, high sensitivity to various kinds of damages. There are two groups of wave modes in plates and shell components: symmetric modes and anti-symmetric modes, which is divided according to the symmetry of particle motion. The physical properties of guided wave propagation (i.e. multimodal, dispersive, scattering, attenuation and so on) can advantageously be used in material characterization, nondestructive evaluation, and structural health monitoring. In order to fully utilize ultrasonic guided waves for practical applications, it is necessary to have a firm grasp of their propagation characteristics, actuating and sensing methods, signal processing and diagnosis tools, and other related techniques. The present Special Session seeks for new findings and novel developments of guided wave related methods and techniques. Towards this end, we welcome submissions on the following topics: theoretical modeling, simulation, measurement and signal processing, and damage visualization. Submissions on other relevant topics, including smart materials, acoustic sensors, and innovative applications, are also welcome.
The topics of interest include, but are not limited to:
Guided wave modelling and propagation
Acoustic sensors
Advanced sensing and testing
Signal processing
Damage visualization
Practical validation and applications
Advanced Techniques for Machinery Prognostics and Health Management Based on Data-driven Methods
Session Organizers:
Assoc. Prof. Yujie Zhang, Sichuan University
Yujie Zhang received the B.E. degree from Harbin Institute of Technology at Weihai, Weihai, China, in 2014, and the Ph.D. degree from Harbin Institute of Technology (HIT), Harbin, China, in 2021. He is currently an associate professor with the College of Electrical Engineering, Sichuan University, Chengdu, China. His current research interests include prognostics and health management, condition monitoring, data-driven degradation modeling and electronic-mechanical system simulation. He is currently an Associate Editor of the IEEE Transactions on Instrumentation and Measurement.
Email: zhangyj@scu.edu.cn
Assoc. Prof. Jingyue Pang, Chongqing Technology and Business University,
Dr. Jingyue Pang received the Ph.D. degree from Harbin Institute of Technology, P. R. China. She is currently an associate professor in the School of Artificial Intelligence, Chongqing Technology and Business University. Her main research interests include engineering testing and signal processing, spacecraft telemetry data analysis, anomaly detection for spacecraft power subsystem, and analysis on industrial big data.
Email:jypang2019@ctbu.edu.cn
Asst. Prof. Benkuan Wang, Harbin Institute of Technology
Benkuan Wang received the Ph.D. degree in information and communication engineering from Harbin Institute of Technology (HIT), Harbin, China, in 2022. He is currently an Assistant Professor with the School of Electronics and Information Engineering, HIT. His research interests include condition monitoring, system health management, embedded high performance computing, and UAS simulation and verification.
Email:wangbenkuan@hit.edu.cn
Prof. Jianyu Long, Dongguan University of Technology
Jianyu Long is currently a full professor at Dongguan University of Technology, where he received the Ph.D. degree in metallurgical engineering from Chongqing University in 2017. From Jan. 2015 to Jan. 2016, he was a visiting scholar at University of Florida, and from Jul. 2017 to Apr. 2019, he was a postdoctoral researcher at South China University of Technology. His research interests include computational intelligence, operations research, prognostics and system health management.
Email:longjy@dgut.edu.cn
Download: Special Session #12.pdf
The reliability and efficiency of industrial machinery are crucial for ensuring smooth operations and minimizing downtime. Traditional methods in machinery health management often rely on predefined models and heuristics, which may struggle to adapt to the complexities of modern machinery systems. With the advent of data-driven methods, there is significant potential to enhance the accuracy and robustness of prognostics and health management (PHM) systems. Data-driven approaches leverage vast amounts of operational data generated by machines, such as sensor data, system logs, and performance metrics, to learn patterns and predict future health trends, enabling proactive maintenance and fault prevention.
This special session seeks to explore the latest developments and applications of data-driven techniques in machinery prognostics and health management. We invite researchers and industry professionals to present their insights, methodologies, and case studies that showcase the integration of data-driven approaches—such as machine learning, deep learning, and statistical modeling—into PHM systems. By fostering collaboration and sharing best practices, we aim to push the boundaries of current machinery health management techniques and contribute to the development of smarter, more efficient maintenance systems for industrial applications.
Suitable topics for this special session include but are not limited to:
Machine Learning Algorithms for Prognostics and Health Management in Industrial Systems
Deep Learning Approaches for Predictive Maintenance and Health Monitoring in Machinery
Anomaly Detection Methods in Industrial Equipment for Prognostics and Health Management
Ensemble Learning Methods for Robust Prognostics and Health Management Systems
Transfer Learning for Prognostics and Health Management in Machinery
Reinforcement Learning for Prognostics and Health Management in Industrial Machinery
Applications of Neural Networks in Machinery Prognostics and Health Management
Advancements in Intelligent Fault Prediction, Diagnosis, and Sustainable Maintenance of Vehicle Critical Systems
Session Organizers:
Prof. Heyan Li
Heyan Li graduated from Beijing Institute of Technology, obtaining his Doctor of Engineering degree in Vehicle Engineering in 2004. He has had visiting scholar experiences at the University of Michigan and the University of Denver. Currently, he serves as a professor at the School of Urban Transportation and Logistics, Shenzhen Technology University, and holds several important positions within the university and professional societies. His research fields span intelligent vehicle technology, vehicle theory and technology, mechanical tribology, and more.
Email: liheyan@sztu.edu.cn
Assoc. Prof. Liang Yu
Liang Yu received his Ph.D. degree in Mechanical Engineering in Beijing Institute of Technology (BIT) in 2021. He was a visiting scholar at Queen’s University Canada from 2017 to 2019. He is currently a associate research fellow of the National Key Laboratory of Intelligent unmanned Systems. His main research interests are unmanned vehicle theory and technology, equipment fault diagnosis technology, vehicle digital twin and intelligent operation and maintenance.
Email: yuliang@bit.edu.cn
Assoc. Prof. Jianpeng Wu
Jianpeng Wu received the Ph.D. degree from Beijing Institute of Technology, P. R. China. He was a visiting scholar at Rice University, US. He is currently an associate professor in the Mechanical Engineering School, Beijing Information Science and Technology University, Beijing, P. R. China. His research interests include vehicle transmission system digital twin, and RUL prediction of mechanical systems and key components.
Email: 15811319103@163.com
Download: Special Session #13.pdf
As the automotive industry advances towards an era of autonomy, electrification, and connectivity, integrating advanced theories, digital tech, and AI in prognostics and health management (PHM) for vehicle critical subsystems like powertrain and chassis is crucial. Malfunctions in key components such as antilock braking systems (ABS) and electric power steering (EPS) can lead to life - threatening accidents. Machine - learning models, using deep neural networks (DNNs) and recurrent neural networks (RNNs), analyze multi - sensor data in real - time to detect early faults, reducing risks. Sustainable maintenance, guided by life - cycle cost analysis and circular economy ideas, cuts environmental impact and long - term costs. These advancements in fault prediction, diagnosis, and maintenance are driving the automotive industry's 21st - century progress.
The topics of interest include, but are not limited to:
AI/ML-based prognostics for engines, transmissions, batteries, and drivetrains
Digital twin-enabled dynamic modeling and failure simulation
Physics-informed neural networks for degradation forecasting
Edge computing and real-time anomaly detection in connected vehicles
Multimodal data fusion (vibration, thermal, acoustic) for fault isolation
Explainable AI (XAI) in fault root cause analysis
Self-learning diagnostic systems for autonomous vehicles
Cybersecurity in vehicle health monitoring networks
Reliability engineering in extreme environments (e.g., autonomous mining trucks)
Fleet-level maintenance decision-making with big data analytics
Artificial self-recovery of high-end mechanical equipment
Session Organizers:
Professor Xin PAN
Xin PAN, Ph.D., professor in the School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Director of the Fault Self-recovery Center of the National Key Laboratory of High end Compressor and System Technology. His research interests includes intelligent diagnosis of equipment faults and vibration self-recovery regulation. He has led more than 10 projects, which are funded by National Basic Research Program Group, National Natural Science Foundation,Natural Science Foundation of Beijing Province, etc. He has published more than 30 journal and international conference paper, holds 10 invention patents and 4 registered software copyrights in China. As the first adult, he has been awarded scientific research awards such as the First Prize for Invention and Innovation by the Chinese Invention Association, the Gold Award at the Geneva International Invention Exhibition, and the Golden Bridge Award by the China Technology Market Association.
E-mail: panxinbuct@163.com
Professor Guangfu BIN
Guangfu BIN, Ph.D., professor and director of the department of scientific research at Hunan University of Science and Technology. His areas of research include rotating machinery dynamics, intelligent fault diagnosis, vibration analysis and self-recovery regulation. He has authored more than 100 archival journals and international conference papers, holds 32 invention patents and 1 registered software copyrights in China. Pro. He hosted and participated in several research projects of National 863 program, 973 program, and the National Natural Science Foundation, etc. He has received 10 provincial and ministerial-level scientific and technological awards, including Hunan Provincial Natural Science Award(Second Prize)for which he was the principal investigator.
E-mail: abin811025@163.com
Professor Weifeng HUANG
Weifeng HUANG, Professor and Associate Department Chair at the Department of Mechanical Engineering, Tsinghua University. He has been selected for the Beijing Outstanding Young Scientist Program and received the Wang Yuming Youth Award for Technology Strengthening the Foundation. With a long-term commitment to research on core foundational technologies for high-end equipment in nuclear power, aerospace, aviation, process industry and marine engineering, he has been honored with four provincial/ministerial-level first prizes. He has published over 100 academic papers and holds over 80 invention patents.
E-mail: huangwf@tsinghua.edu.cn
Download: Special Session #14.pdf
With the continuous development of major equipment in mechanical discipline towards high parameters and intelligence, the dynamic behavior of mechanical system has become more and more complex. The traditional way of manual troubleshooting has been far from meeting the urgent needs of engineering development. Artificial self-recovery is to endow the machine with the ability to maintain a healthy state through bionic design on the basis of fault mechanism and risk analysis.The popularization and application of artificial self-recovery is a disaster reduction and efficiency project to prevent faults and reduce maintenance, and it is an inevitable trend to promote the development of mechanical discipline. In this section, we aim to provide a forum for colleagues to report the most up-to-date research in artificial self-recovery of high-end mechanical equipment. Both original contributions with theoretical novelty and practical solutions for addressing particular problems are solicited.
The topics of interest include, but are not limited to:
Intelligent diagnosis of fast and accurate tracing of typical faults.
Dynamic balance and automatic balance of rotor system.
Active vibration control of high-end equipment.
Principles, methods, and applications of equipment self-repair technology.
Identification and self-recovery regulation of abnormal working conditions for high-end equipment.
Equipment self-protection/compensatory technology.
Safety, Reliability and Intelligent Operation and Maintenance Technology for Autonomous Unmanned Systems
Session Organizers:
Dr. Huan Wang
Dr. Huan Wang is a postdoctoral at City University of Hong Kong. He received his Ph.D. from
Tsinghua University in 2024 and has participated in joint training programs at KU Leuven in
Belgium and the Hong Kong University of Science and Technology. He focuses on the digitalization
and intelligentization of complex industrial systems based on artificial intelligence. He has received
several prestigious awards, including the Gold Medal at the 2024 Geneva International Exhibition
of Inventions, two Gold Medals at the 2024 National Invention Exhibition, and two Third Prizes of
the 2024 China Invention Association Science and Technology Award, China-Japan Friendship
NSK Mechanical Engineering Outstanding Paper Award.
Email: wh.2021@tsinghua.org.cn
Assoc. Prof. Hui Wu
Dr. Hui Wu is an Associate Professor at the School of Economics and Management, Harbin Institute
of Technology (Weihai). She obtained her Ph.D. from Tsinghua University in 2022. Her research
focuses on large-scale complex system modeling, online monitoring, anomaly detection, fault
prediction, and health management using statistical learning and artificial intelligence methods. Her
academic achievements have been recognized with awards at prestigious conferences, including
those organized by the Institute for Operations Research and the Management Sciences (INFORMS),
the Institute of Industrial and Systems Engineers (IISE), and the China Society of Optimization,
Overall Planning, and Economic Mathematics.
Email: wuh@hit.edu.cn
Assoc. Prof. Ying Zhang
Dr. Ying Zhang is currently an Associate Professor with the School of Mechanical Engineering,
University of Science and Technology Beijing. She completed the Ph.D. degree with the School of
Mechanical and Mechatronic Engineering, University of Technology Sydney, Australia in 2021. Her
main research interests include intelligent fault diagnosis of industrial machinery, machine learning,
prognostics and health management.
Email: ying.zhang@ustb.edu.cn
Prof. YanFu Li
Dr. YanFu Li is the Director of the Institute for Quality and Reliability and a tenured professor in
the Department of Industrial Engineering at Tsinghua University. His research focuses on system
reliability, prognostics and health management (PHM), and maintenance decision-making, with
applications across various industrial and engineering systems. Dr. Li serves as an Associate Editor
for leading journals, including IEEE TII, RESS, and IEEE TR (2017–2024). He is also the Chair
of the IEEE Technology and Engineering Management Society (Beijing Section), Vice Chair of the
System Reliability Committee of the Chinese Society of Systems Engineering, and an expert
reviewer for the China Quality Award.
Email: liyanfu@tsinghua.edu.cn
Download: Special Session #15.pdf
With the rapid advancement of technologies such as artificial intelligence, the Internet of Things, and edge computing, the use of autonomous systems (e.g., autonomous vehicles, drones, industrial robots) is expanding in fields like smart manufacturing, intelligent transportation, and defense. However, the highly dynamic, open, and complex operating environments of these systems pose significant challenges to safety, reliability, and intelligent operation and maintenance (IOM). Traditional rule-based control and maintenance methods struggle to address issues like multimodal environmental disturbances, system degradation, and human-machine collaboration conflicts. There is an urgent need to advance key technologies for dynamic safety assurance, reliability modeling and prediction, and data-driven intelligent operation and maintenance to ensure the robustness and trustworthiness of autonomous systems in complex scenarios. Safety research focuses on fault tolerance and adaptability under unknown disturbances, reliability research aims to understand failure propagation mechanisms under multi-source uncertainties, and intelligent operation and maintenance technologies integrate real-time monitoring and decision optimization for sustainable system performance. This topic emphasizes developing an integrated "design-operation-maintenance" framework to transition autonomous systems from functional implementation to trusted services, providing theoretical support and technical assurance for future critical infrastructure in intelligent societies.
The topics of interest include, but are not limited to:
Real-time Safety Situation Awareness and Dynamic Risk Assessment Based on Edge
Computing
Security Game and Fault-tolerance Mechanisms in Multi-Agent Collaborative Scenarios
System Reliability Simulation and Verification Based on Digital Twin
Anomaly Detection and Fault Diagnosis Driven by Small Sample/Unsupervised Learning
Reliable Fault Diagnosis Methods Based on Advanced Deep Learning Techniques
Preventive Maintenance Strategy Optimization through Multi-modal Data Fusion
Edge-Cloud Collaborative Autonomous Operation and Maintenance Decision Framework
Joint Optimization Theory for Safety, Reliability, and Maintenance
Intelligent Operation and Maintenance Combining Knowledge Graphs and Causal Inference
Intelligent Operation and Maintenance Driven by Multi-modal Large Models
Physics-inspired Multi-modal Intelligent Fault Diagnosis of Machinery
Session Organizers:
Dr. Haihong Tang
Dr Haihong Tang received the Ph.D. degree from Mie University, Tsu, Japan, in 2021. She has been an associate professor with Zhejiang Ocean University, Zhoushan, China. The topic of her research is intelligent fault diagnosis for machinery and signal processing.
Email: tangyanlihaihong@163.com
Dr. Kun Zhang
Dr Kun Zhang received the Ph.D. degree from Mie University, Tsu, Japan, in 2022. He is currently a lecturer at the College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China. His research interests include condition monitoring and fault diagnosis of rotating equipment such as bearings and gears, and digital signal processing.
Email: zkun212@bjut.edu.cn
Prof. Bangjun Lei
Dr Bangjun Lei received the Ph.D degree in control science and engineering from Southeast University, Nanjing, Jiangsu, China, in 2014. He is a professor in the School of Information Engineering, Zhejiang Ocean University, Zhoushan, China. His main research interests include modeling and control of nonlinear systems, new energy generation and operation control, intelligent control, etc.
Email: bangjunlei@yeah.net
Prof. Yonggang Xu
Dr. Yonggang Xu received the B.S. and Ph.D. degrees in mechanical engineering and automation from Xi’an Jiaotong University, Xi’an, China, in 1998 and 2003, respectively. From 2004 to 2006, he worked with the Post-Doctoral Research Station, Tianjin University, Tianjin, China. Since 2006, he has been a Professor with the College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing, China. His research interests include condition monitoring and fault diagnosis of electromechanical equipment and modern signal processing.
Download: Special Session #16.pdf
Multimodal monitoring data (sound signals, vibration signals, thermal imaging, etc.) fully records a large amount of information radiated by multiple physical sources under different working conditions. If the multimodal data reflecting the operating status of the main bearing can be fully excavated, it can achieve comprehensive and more precise intermittent fault feature extraction and diagnosis. Mover, it is beneficial to embed physical information of mechanical faults into intelligent fault diagnosis for improving and interpreting diagnostic models. Submissions related to advanced and emerging technologies, as well as their practical applications in the studies described are encouraged.
The topics of interest include, but are not limited to:
Methodologies for fusion in multimodal representation learning of fault diagnosis
Multimodal feature aligning for fault diagnosis
Approaches for multimodal feature transfer
Physics-inspired deep learning for fault diagnosis
The interpretability of the physics-inspired multimodal fault diagnosis model
Other innovative technologies for physics-inspired multimodal fault diagnosis
Advanced intelligent methodologies for fault detection, diagnosis and RUL prediction of aerospace systems
Session Organizers:
Dr. Dandan Peng
Dr. Dandan Peng is currently a Postdoctoral Fellow at The Hong Kong Polytechnic University. She received the Ph.D. degree in Mechanical Engineering at KU Leuven in Belgium. Her research interests focus on AI-based fault prognosis and health management (PHM) for industrial systems, with applications in aerospace systems, wind turbines, high-speed trains, and energy storage systems. She has published about 30 SCI/EI papers, including six highly cited papers. Her contributions have earned prestigious awards, such as the 2024 European Academy of Wind Energy Scholarship, the 2024 ASME Turbo Expo Student Travel Award, the Best Paper Award at North America PHM2022, and China Association of Inventions Science and Technology Awards (2024).
Email: dandanpeng2@gmail.com
Prof. Chenyu Liu
Dr. ChenYu Liu is a Professor at the School of Mechanical and Electrical Engineering, Northwestern Polytechnical University (NPU). He earned his Ph.D. from KU Leuven in Belgium in 2023. He has long been engaged in research on artificial intelligence algorithms and intelligent operation and maintenance management for equipment, with a focus on applications in intelligent fault diagnosis, intelligent non-destructive testing of aerospace materials, and intelligent testing of aerospace equipment.
Email: chenyuliu@nwpu.edu.cn
Prof. Chuanjiang Li
Dr. Chuanjiang Li, an associate professor at State Key Laboratory of Public Big Data, Guizhou University, a joint Ph.D. at KU Leuven, Belgium, mainly research on UAV big data, low altitude equipment, UAV intelligent operation and maintenance and digital twin. He has published 2 books and more than 30 SCI search papers, and 3 papers selected as ESI Global Top1% Highly Cited Papers. Member of the youth editorial board of Scientific Reports, Smart Construction, Unmanned Systems Technology.
Email: licj@gzu.edu.cn
Dr. Zhiyuan He
Dr. Zhiyuan He is a lecturer at the College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics (NUAA). His research focuses on aircraft fault monitoring and diagnosis, vibration signal processing, and fatigue and damage analysis. He has led one National Natural Science Foundation of China (NSFC) Youth Program and contributed to two National Science and Technology Major Project and two NSFC General Programs. With 13 SCI/EI-indexed publications, he was awarded the 2024 Jiangsu Provincial Outstanding Postdoctoral Program for his academic excellence.
Email: hzy2017@nuaa.edu.cn
Assoc. Prof. Zhiliang Liu
Dr. Zhiliang Liu is currently an Associate Professor and Ph.D. supervisor at the University of Electronic Science and Technology of China (UESTC). He completed his Ph.D. jointly at the University of Alberta and UESTC. He is a Fellow of the International Society of Engineering Asset Management, a global top 2% scientist, a candidate for the Sichuan Province Academic and Technical Leader, and a Senior Member of IEEE. His main research focuses on intelligent maintenance of complex electromechanical equipment.
Email: Zhiliang_Liu@uestc.edu.cn
Download: Special Session #17.pdf
Aerospace systems, including aircraft and spacecraft, are critical to modern transportation, defense, and exploration. However, the extreme operating conditions, high reliability requirements, and complex system interdependencies pose significant challenges to their operation and maintenance. Traditional condition monitoring and fault diagnosis methods often struggle to cope with the dynamic environments, limited data availability, and stringent safety standards associated with aerospace systems. This special session aims to bring together researchers and industry experts to explore the latest advancements in intelligent diagnostics and prognostics for aerospace systems. We seek to foster discussions on innovative data-driven and physics-informed approaches that can enhance the reliability, safety, and cost-effectiveness of aerospace operations.
The topics of interest include, but are not limited to:
Machine learning and deep learning algorithms for anomaly detection, fault classification, and failure prediction
Multi-modal data fusion for comprehensive condition assessment of aerospace systems
Transfer learning and domain adaptation for fault diagnosis and remaining useful life (RUL) prediction in diverse operating conditions
Explainable AI (XAI) for interpretable fault diagnosis and decision-making in safety-critical aerospace applications
Digital twin technology for virtual representation and real-time monitoring of aerospace systems
Physics-informed neural networks for degradation modeling and remaining useful life prediction of aerospace components
Uncertainty quantification and risk assessment for aerospace system operations under extreme conditions
Fleet-level maintenance decision-making using big data analytics for improved operational efficiency
Any other related topics
Advanced modeling, simulation, state assessment, and prognosis techniques for energy storage systems
Session Organizers:
Prof. Lei Mao
Prof. Lei Mao received the B.Sc. degree from the Hefei University of Technology, Hefei, China, in 2004, the M.Sc. degree from the University of Science and Technology of China, Hefei, in 2007, and the Ph.D. degree from the University of Edinburgh, Edinburgh, U.K., in 2012. From 2012 to 2013, he was a Research Associate with the University of Portsmouth, Portsmouth, U.K. From 2013 to 2018, he was a Research Associate with the Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, U.K. Since 2018, he has been a Professor with the Department of Precision Machinery and Precision Instruments, University of Science and Technology of China. His research interests include system reliability analysis, development of intelligent health management systems, and fault diagnostic and prognostic techniques. Dr. Mao is a member of the Fault Diagnosis Committee of China Vibration Engineering Association and International Association of Engineers.
Email: leimao82@ustc.edu.cn
Dr. Heng Zhang
Heng Zhang received the B.E. degree from Harbin Engineering University, Harbin, China, in 2016, and the Ph.D. degree from the Sichuan University, Chengdu, China., in 2021. Dr. Zhang is an associate professor at the College of Electrical Engineering, Sichuan University, Chengdu, China. His research focuses on battery management system, prognostics and health management.
Email: hengzhang27@scu.edu.cn
Dr. Zhiyong Hu
Dr. Zhiyong Hu received the M.E. degree in Precision Instrument and Machinery from University of Science and Technology of China in 2016, and Ph.D. in the department of Industrial, Manufacturing and Systems Engineering from Texas Tech University in 2021. He was a postdoctoral researcher in the Department of Precision Machinery and Precision Instrumentation in University of Science and Technology of China from 2021 to 2024. Currently, he is with the Department of Automation at Anhui University. His research interests mainly focus on statistical modelling of multi-stream data analytics for the monitoring, analysis and control of complex systems.
Email: hzyllwen@ahu.edu.cn
Download: Special Session #18.pdf
The use of Energy Storage Systems (ESSs) is pervasive in many application domains such as e-mobility, robots and drones, renewable energy systems. As such, reliability and state assessment for these new ESSs are key steps in ensuring system safety, improving performance, reducing costs, and supporting intelligent operation and maintenance. One of the current challenges is to identify models of ESSs, in particular for batteries, that strike a balance between accuracy and ease of implementation in real control systems. What’s more, the multifarious and uncertain user demands increase their complexity of degradation tendencies and failure, and pose a challenge to high-precision life and reliability assessment. To address these challenges, theoretical and applied researches on advanced modeling, simulation, state evaluation and prediction technologies for the ESSs are needed and particularly welcome to be presented in this special session.
Suitable topics include but are not limited to:
Modeling of the degradation and failure mechanisms for batteries
Advanced state estimation methods for new ESSs, and their core components
Advanced reliability assessment methods for new ESSs, and their core components
Aging prediction for batteries based on advanced machine learning methods
Multi-physics and multi-scale simulation technologies for batteries
Advanced sensing and fault diagnosis technologies for new ESSs
Data-driven fault diagnosis and prognosis for electromechanical coupling systems: multi-physics modeling and multi-modal fusion
Session Organizers:
Prof. Jianping Xuan
Dr. Jianping Xuan received the Ph.D. degree in mechanical engineering from Huazhong University of Science and Technology, P.R. China. He was a visiting scientist with the Department of Mechanical Engineering, Massachusetts Institute of Technology between 2013-2014. He is currently a Professor with School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China. His research interests include PHM of Mechanical Equipment, Deep Learning, Finite Element Analysis, Time-Frequency Signal Analysis, and Industrial Big Data Processing.
Email: jpxuan@hust.edu.cn
Dr. Zisheng Wang
Dr. Zisheng Wang received the Ph.D. degree in mechanical engineering from Huazhong University of Science and Technology, P.R. China. He is currently an assistant researcher at the Department of Industrial Engineering, and a postdoctoral fellow at the Quality and Reliability Research Institute, Tsinghua University, Beijing, China. His research interests include Intelligent Operation and Maintenance of High-End Equipment, Digital Twin, Industrial Large-Scale Model, and System Reliability Assessment.
Email: zisheng8@mail.tsinghua.edu.cn
Download: Special Session #19.pdf
Electromechanical coupling systems, such as high-end CNC machine tools and robotic systems, exhibit strong coupling characteristics across thermal, mechanical, and electrical multi-physical fields, with their faults often displaying nonlinear propagation features across physical domains. Traditional single-domain analysis methods struggle to accurately capture fault evolution patterns. Multi-physical field modeling and multi-modal data fusion techniques offer innovative pathways to address these challenges: the former can reveal the dynamic coupling mechanisms between mechanical vibration, current fluctuations, and thermal field distributions, laying a theoretical foundation for fault mechanism research; the latter significantly enhances diagnostic and predictive accuracy by integrating heterogeneous signals such as vibration, acoustic emission, current, and vision to construct cross-domain feature spaces. Although data-driven models have enhanced real-time monitoring capabilities, they currently face three major technical bottlenecks: inadequate analysis of fault propagation mechanisms across physical domains, a lack of adaptive fusion methods for multi-source heterogeneous data, and the urgent need to enhance the interpretability of AI diagnostic results. This Special Session focuses on cross-innovation in multi-physical field modeling, multi-modal fusion, and intelligent algorithms, aiming to break through the theoretical framework for cross-domain fault analysis in electromechanical coupling systems and develop multi-modal intelligent diagnostic methods for varying operating conditions to build an interpretable and trustworthy health management decision support system. Submissions on related topics, such as multi-physical field dynamics and defect evolution, self-supervised multi-modal feature decoupling, and interpretable and trustworthy diagnostic technologies are also warmly welcomed. The topics of interest include, but are not limited to:
Physics modeling and simulation
Multi-source data acquisition and fusion
Physics-informed machine learning
Cross-domain fault diagnosis with feature generalization
Open-set fault diagnosis under unknown classes
Practical validation and applications
Intelligent sensing and data analytics for damage detection and prognostics of mechanical equipment
Session Organizers:
Yuejian Chen, University of Manitoba
Email: Yuejian.Chen@umanitoba.ca
Yuanjin Ji, Tongji University
Email: jiyuanjin@tongji.edu.cn
Rui Yuan, Wuhan University of Science and Technology
Email: yuanrui@wust.edu.cn
Jie Wang, University of Manitoba
Email: j.wang@umanitoba.ca
Download: Special Session #20.pdf
The complex behavior of mechanical equipment under alternating loads and varying environmental conditions presents significant challenges for traditional monitoring and prognostics methods. Advances in intelligent sensing technologies—such as Fiber Bragg grating sensors, piezoelectric sensors, MEMS sensors, and wireless sensor networks—have greatly enhanced structural health monitoring (SHM) by capturing high-resolution structural responses in real time. However, the exponential growth in monitoring data necessitates advanced techniques for data extraction, analysis, and decision-making.
This special session aims to explore recent advancements in damage detection, feature identification, and remaining useful life (RUL) prediction for mechanical systems. We invite researchers and practitioners to submit their latest findings on these topics. Contributions that address the accuracy, reliability, and practical implementation of predictive models in real-world applications are particularly encouraged.
Topics of Interest:
Intelligent sensing technologies for real-time SHM
Advanced data analytics and signal processing methods
RUL prediction models and their robustness under varying conditions
The integration of explainable AI for enhanced model transparency and industrial adoption
Applications in mechanical, aerospace, energy, and civil engineering
Key Technologies for the Operation and Maintenance of Industrial Robots
Session Organizers:
Guolin HE
University: South China University of Technology
Research Interests: Dynamics of mechanical systems, signal processing and fault diagnosis technology, predictive maintenance and health management, and digital twin.
Email: hegl@scut.edu.cn
Kai WU
University: South China University of Technology
Research Interests: Industrial robotic machining and motion control, robot assisted manufacturing and manufacturing execution system.
Email: whphwk@scut.edu.cn
Chuan LI
University: Dongguan University of Technology
Research Interests: Health monitoring of mechanical equipment, big data processing, data mining, and intelligent optimization.
Email: chuanli@dgut.edu.cn
Download: Special Session #21.pdf
Amidst the tide of intelligent manufacturing and Industry 4.0, industrial robots have emerged as the core vehicle of modern manufacturing, with their operational efficiency and reliability being directly linked to the productivity of production lines and the competitive edge of enterprises. However, as the application scenarios of robots become more complex and extensive, traditional maintenance methods are encountering formidable challenges: the inability to process multi-source heterogeneous data in real-time, an over-reliance on expert experience for fault tracing and diagnosis, difficulty in accurately predicting the performance degradation of critical components, a lag in the global optimization of collaborative maintenance within clusters, and the absence of large-scale models for the maintenance domain of industrial robots, among other issues, are becoming increasingly evident. Overcoming technical barriers and establishing an integrated intelligent maintenance system that encompasses "diagnosis-prediction-decision-control" have become shared challenges in both academia and industry.
This special session, focusing on key technologies and applications in industrial robot maintenance, aims to explore new paradigms that deeply integrate physical principles with data-driven approaches, promoting the in-depth integration of cutting-edge technologies such as digital twins and large language models with industrial scenarios.
We invite contributions from researchers, and industry professionals to share their expertise and experiences in key technologies of fault diagnosis, degradation prediction, spare parts planning, and the application of large language models for the operation and maintenance of industrial robots.
Topics of Interest:
Review and Perspective of Predictive Maintenance and Health Management of Industrial Robots
Digital Twin Frameworks and Architecture Applications for Data Acquisition and Fault Traceability of Industrial Robots
Physical Knowledge-driven Dynamics Modelling of Key Components in Industrial Robot Systems
Physics Informed Condition Monitoring and Fault Diagnosis of Industrial Robots
Anomaly Detection and Early Warning for Key Components of Industrial Robots
Remaining Useful Life Prediction for Key Components of Industrial Robots
Error Compensation and Precision Recovery of Industrial Robots
Optimization of Timing Collaborative Maintenance and Spare Parts Planning for Industrial Robot Swarm
Applications of Large Language Model and Industrial Large Model in Predictive Maintenance and Health Management of Industrial Robots
Any other related topics
System dynamics, control and diagnosis of key components of rail vehicle
Session Organizers:
Prof. Zhognkui Zhu
Dr. Zhongkui Zhu received the Ph.D. degree in instrumentation science and technology University of Science and Technology of China. . He is currently a Professor with School or Rail Transportation, Soochow University, Suzhou, China. His research interests include condition monitoring and fault diagnosis of railway vehicle transmission, railway vehicle system dynamics.
Email: zhuzhonkgui@suda.edu.cn
Prof. Zaigang Chen
Dr. Zaigang Chen received the Ph.D. degree in mechanical engineering from Chongqing University, P.R. China. He was a visiting Ph.D student with the Department of Mechanical Engineering, University of Cincinnati between 2010-2012. He is currently a Professor with State Key Laboratory of Rail Transit Vechile System, Southwest Jiaotong University, Chengdu, China. His research interests include mechanical transmission system dynamics, railway vehicle system dynamics, mechanical fault diagnosis.
Email: zgchen@home.swjtu.edu.cn
Director Zhang Zhiqiang
Zhang Zhiqiang, senior engineer (professor level), serves as the deputy director of the CRRC Qingdao Sifang Co., Ltd. Engineering Laboratory. The expert listed in the Ministry of Science and Technology's database.The main research areas include: health monitoring and intelligent operation and maintenance of railway vehicles, railway vehicle testing technology, and railway vehicle dynamics.He has presided over multiple major scientific research projects of CRRC and national research projects.
Email: zhangzhiqiang@cqsf.com
Download: Special Session #22.pdf
Economic and social development in most countries has increased considerably the requirement for transportation capability. Rail vehicle has played an important role in this development due to its strong transportation capability and high speeds. The performance of rail vehicle hinges on the integrated behavior of key components such as suspension systems, traction systems, braking systems, wheel-rail interfaces and so on. Failures in any of these key components can result in unexpected breakdowns, which can lead to serious traffic accidents. System dynamics, control and diagnosis of key components of rail vehicle have recently come to play a crucial role. This special topic focuses on advancing the understanding and optimization of rail vehicle dynamics, developing robust control strategies, and implementing intelligent diagnostic methodologies to ensure service safety of rail vehicle. By addressing challenges in dynamics modeling, real-time control, and reliable diagnosis, this field aims to enhance the sustainability, safety, and economic viability of modern rail transportation systems.
The topics of interest include, but are not limited to:
Vehicle system dynamics modeling and simulation
Vibration and noise control
Intelligent control strategies
Rail vehicle monitoring and vibration signal processing
Intelligent early fault detection and diagnosis
Interpretable deep learning for rail vehicle fault diagnosis
Wheel-rail interface dynamics and wear management
Practical validation and applications
Mechanism and Data-Driven Intelligent Diagnosis and Prediction of Complex Systems
Session Organizers:
Haidong Shao, Hunan University
Research Interests: Industrial big data processing; Intelligent diagnosis and prognosis of complex systems
Email: hdshao@hnu.edu.cn
Zhuyun Chen, Guangdong University of Technology
Research Interests: Dynamical signal processing; Intelligent fault diagnosis and prognosis
Email: mezychen@gdut.edu.cn
Yun Kong, Beijing Institute of Technology
Research Interests: Fault Diagnosis, Prediction, and Intelligent Operation & Maintenance for High-end Equipment
Email: kongyun@bit.edu.cn
Te Han, Beijing Institute of Technology
Research Interests: Multimodal Data Fusion and Fault Diagnosis for New Energy Systems
Email: hante@bit.edu.cn
Junyu Qi, Reutlingen University
Research Interests: Self-Sensing-Driven Health Monitoring and Intelligent Diagnosis
Email: junyu.qi@reutlingen-university.de
Download: Special Session #23.pdf
In the era of rapid technological advancement, complex systems are becoming increasingly prevalent across various industries, from aerospace and automotive to healthcare and energy. These systems are characterized by intricate interactions, high levels of integration, and the need for real-time monitoring and control. Traditional diagnostic methods often fall short in handling the complexity and dynamic nature of these systems. Mechanism and data-driven intelligent diagnosis and prediction offer a promising solution by combining the strengths of mechanistic models and data analytics to enhance the accuracy and reliability of system diagnostics and predictive maintenance.
The topics of interest include, but are not limited to:
Hybrid Modeling Techniques for enhanced fault diagnosis
Advanced data analytics techniques for fault detection and diagnosis
Application of mechanistic models in fault diagnosis and predictive maintenance.
Real-time intelligent diagnostic systems for complex systems.
Predictive maintenance strategies based on hybrid models and data analytics.
Practical applications of mechanism and data-driven intelligent diagnosis in various industries.