Special Session List
Special Session #01
Advanced Technologies in Battery Fault Diagnosis and Health Management
Session Organizers:
Zhongwei Deng, Associate Professor, University of Electronic Science and Technology of China
Email: dengzw1127@uestc.edu.cn
Penghua Li，Professor，Chongqing University of Posts and Telecommunications
Email: lipenghua88@163.com
Email: lipenghua88@163.com
Jinhao Meng, Associate Professor, Xi’an Jiaotong University
Email: jinhao@xjtu.edu.cn
Jiwei Wang, Associate Professor, Xinjiang University
Email: jiweiwang@xju.edu.cn
Download:Special Session #01.pdf
Introduction of Special Session #01
With the increasing importance of battery technology in electric vehicles, energy storage systems, and smart mobile devices, ensuring battery reliability has become critical for system safety, reliability, and efficient operation. This special session aims to provide a high-level academic platform for experts, scholars, engineers, and industry representatives to discuss the latest advances in battery fault diagnosis and health management, bridging the gap between theoretical research and practical application.
Conference Objectives:
- Cutting-Edge Discussions: To exchange in-depth insights into the mechanisms behind battery failures, advanced fault detection techniques, and innovative methods for battery state evaluation and health management.
- Application Sharing: To showcase successful implementations of advanced battery monitoring technologies, data-driven approaches, and intelligent diagnostic tools in real-world engineering scenarios.
- Interdisciplinary Collaboration: To foster collaboration among academia, research institutions, and industry, exploring multidisciplinary approaches to address current technical challenges in battery health management.
- Future Perspectives: To discuss emerging trends in early warning systems, remaining useful life prediction, and risk control strategies, thereby laying the groundwork for future technological advancements in battery safety and performance.
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Key Topics to Be Covered:
- Battery Fault Diagnosis Techniques
- Fault detection methods based on traditional signal processing and statistical analysis.
- Applications of machine learning and deep learning for battery fault recognition.
- Multi-sensor data fusion and fault pattern recognition technologies.
- Battery Health Management and State Evaluation
- Techniques for assessing battery state-of-health (SoH) and predicting remaining useful life.
- Health management strategies based on electrochemical models and data-driven approaches.
- The role of real-time monitoring systems and advanced data acquisition technologies.
- Failure Mechanism Analysis and Case Studies
- In-depth exploration of battery failure mechanisms and root cause analysis.
- Presentation and discussion of typical fault cases and successful diagnostic experiences.
- Early Warning Systems and Risk Control
- Design and implementation of early warning systems for battery faults.
- Evaluation of fault risks and development of effective emergency response and safety management strategies.
- Industrial Applications and Future Trends
- Practical applications of battery fault diagnosis in electric vehicles and energy storage systems.
- Innovations in battery management systems (BMS) and future development directions.
- Battery Fault Diagnosis Techniques
- By hosting this special session, we aim to establish a collaborative, interdisciplinary forum that not only disseminates cutting-edge theoretical findings but also delivers practical solutions to real-world challenges in battery management. This initiative is expected to significantly contribute to the advancement of battery fault diagnosis and health management technologies, thereby enhancing the overall safety, stability, and efficiency of battery systems across various high-demand applications.
Special Session #02
Intelligent fault diagnosis and health management of new energy systems
Session Organizers:
Associate Prof ,Guoqian Jiang,Yanshan University
E-mail: jiangguoqian@ysu.edu.cn
Prof ,Xiaohang Jin, Zhejiang University of Technology
E-mail: xhjin@zjut.edu.cn
Prof ,Zhengguo Xu,Zhejiang University
E-mail: xzg@zju.edu.cn
Prof ,Xu Cheng, Tianjin University of Technology
E-mail:xu.cheng@ieee.org
Download:Special Session #02.pdf
Introduction of Special Session #02
In the context of the global energy structure toward green and low-carbon transformation, the large-scale application of new energy systems centered on wind power, photovoltaics, energy storage, and hydrogen energy has become a critical pathway to achieving the "dual-carbon" goals.. However, new energy systems face significant challenges in fault diagnosis and health management due to their complex equipment structures, highly variable operating conditions, frequent environmental disturbances, high-dimensional data complexity, dynamic modeling difficulties, poor interpretability of diagnostic models, low accuracy in lifespan prediction, and delayed maintenance decisions. The deep integration of technologies such as artificial intelligence (AI), large language models (LLM), digital twins, the Internet of Things (IoT), and edge intelligence has created opportunities for theoretical breakthroughs and technological innovation in lifecycle health management for new energy systems. This special session focuses on fault diagnosis, health management, and safety optimization throughout the entire lifecycle of new energy systems. It aims to gather cutting-edge research achievements from academia and industry worldwide, foster discussions on innovative methods and practical applications for deeply integrating intelligent technologies with new energy scenarios, and address emerging challenges and future research directions. The goal is to advance the development of safer, more efficient, and more reliable new energy systems.
Special Session #03
Advancements in Intelligent Operation and Health Management for Aerospace Systems
Session Organizers:
Dandan Peng, Postdoctoral Fellow, The Hong Kong Polytechnic University
E-mail:dandanpeng2@gmail.com
Chenyu Liu, Professor, Northwestern Polytechnical University
E-mail:chenyuliu@nwpu.edu.cn
Teng Wang, Associate Professor, Northwestern Polytechnical University
E-mail:wangteng@nwpu.edu.cn
Xiang Sheng, Lecturer, Chongqing University of Posts and Telecommunications
E-mail:xiangsheng@cqupt.edu.cn
Download:Special Session #03.pdf
Introduction of Special Session #03
With the rapid advancement of aerospace technology, the increasing complexity and intelligence of aircraft systems have made fault prognosis and health management (PHM) technologies a cornerstone for ensuring the safety, reliability, and efficient operation of aerospace systems. This session focuses on cutting-edge technologies and applications of PHM in aerospace systems, aiming to address challenges posed by high dynamics, strong coupling, and extreme environmental adaptability, such as multi-source heterogeneous data processing, complex fault pattern recognition, dynamic modeling, and high-precision lifetime prediction. The session will delve into the latest research achievements in aerospace PHM, covering fault mechanism studies, intelligent fault detection technologies, state evaluation methods, and remaining useful life prediction, with a particular emphasis on the innovative application and breakthroughs of emerging technologies like artificial intelligence and digital twins. These efforts provide a solid theoretical foundation and technical reserve for the safe, reliable, and efficient operation of aerospace systems. By establishing a high-level academic exchange platform, this session is dedicated to advancing the innovative development of aerospace PHM technologies, addressing critical issues in engineering practice, and injecting new momentum into industry progress.
Special Session #04
Advanced High-End Equipment Measurement, Monitoring, Diagnosis and Maintenance Technology
Session Organizers:
Huan Wang, Postdoctoral fellow, City University of Hong Kong
E-mail:wh.2021@tsinghua.org.cn
Te Han, Associate Professor, Beijing Institute of Technology
E-mail:hante@bit.edu.cn
Xiaoyu Jiang, Associate Researcher, Beijing University of Aeronautics and Astronautics
E-mail:wh.2021@tsinghua.org.cn
Te Han, Associate Professor, Beijing Institute of Technology
E-mail:hante@bit.edu.cn
Xiaoyu Jiang, Associate Researcher, Beijing University of Aeronautics and Astronautics
E-mail:jiangxiaoyu@buaa.edu.cn
Yifan Li, Professor, Southwest Jiaotong University,
E-mail:liyifan@swjtu.edu.cn
Yifan Li, Professor, Southwest Jiaotong University,
E-mail:liyifan@swjtu.edu.cn
Download:Special Session #04.pdf
Introduction of Special Session #04
With the rapid advancement of aerospace technology, the increasing complexity and intelligence of aircraft systems have made fault prognosis and health management (PHM) technologies a cornerstone for ensuring the safety, reliability, and efficient operation of aerospace systems. This session focuses on cutting-edge technologies and applications of PHM in aerospace systems, aiming to address challenges posed by high dynamics, strong coupling, and extreme environmental adaptability, such as multi-source heterogeneous data processing, complex fault pattern recognition, dynamic modeling, and high-precision lifetime prediction. The session will delve into the latest research achievements in aerospace PHM, covering fault mechanism studies, intelligent fault detection technologies, state evaluation methods, and remaining useful life prediction, with a particular emphasis on the innovative application and breakthroughs of emerging technologies like artificial intelligence and digital twins. These efforts provide a solid theoretical foundation and technical reserve for the safe, reliable, and efficient operation of aerospace systems. By establishing a high-level academic exchange platform, this session is dedicated to advancing the innovative development of aerospace PHM technologies, addressing critical issues in engineering practice, and injecting new momentum into industry progress.
Special Session #05
Perception, Diagnosis, and Self-Healing Control of Unmanned Systems and High-End Equipment
Session Organizers:
Lingxia Mu, Associate Professor, Xi’an University of Technology
E-mail:mulingxia@xaut.edu.cn
Ban Wang, Associate Professor, Northwestern Polytechnical University
E-mail:wangban@nwpu.edu.cn
Yujiang Zhong, Associate Professor, Northwestern Polytechnical University
E-mail:zhongyujiang@nwpu.edu.cn
Zengwang Jin, Associate Professor, Northwestern Polytechnical University
E-mail: jin_zengwang@nwpu.edu.cn
Download:Special Session #05.pdf
Introduction of Special Session #05
The perception, diagnosis, and self-healing control of unmanned systems and high-end equipment are important indicators of their level of intelligence and the core technological support for achieving autonomous operation. With the rapid development of strategic emerging industries such as the low-altitude economy and intelligent manufacturing, intelligent systems like unmanned aerial vehicles (UAVs) and high-end silicon single-crystal growth equipment are facing increasingly complex application scenarios. They need to not only cope with complex environments such as strong electromagnetic interference, extreme temperatures and pressures, and multi-physical field coupling, but also meet stringent requirements for high reliability, strong real-time performance, and autonomous fault tolerance.
Under this context, intelligent perception, diagnostic prediction, and self-healing control technologies have become the key to breaking through the performance bottlenecks of these systems. This special topic focuses on intelligent perception technologies under challenging tasks, efficient diagnosis under multi-fault coupling and cross-scale propagation, and self-healing control under performance constraints. It aims to provide technological support for the low-altitude economy, intelligent manufacturing, and other fields to advance towards higher levels of autonomous intelligence.
Special Session #06
Perception, Diagnosis, and Self-Healing Control of Unmanned Systems and High-End Equipment
Session Organizers:
Zhang Heng, Associate Professor, Sichuan University
Email: hengzhang27@scu.edu.cn
Email: hengzhang27@scu.edu.cn
Zhang Yujie, Associate Professor, Sichuan University
Email: zhangyj@scu.edu.cn
Email: zhangyj@scu.edu.cn
Pei Hong, Lecturer, Rocket Force University of Engineering
Email: ph2010hph@sina.com
Email: ph2010hph@sina.com
Wang Jianyu, Associate Professor, Sichuan University
Email: wangjianyu@scu.edu.cn
Email: wangjianyu@scu.edu.cn
Wu Zeyu, Assistant Professor, Beihang University
Email: wuzeyu@buaa.edu.cn
Email: wuzeyu@buaa.edu.cn
Miao Jianguo, Lecturer, Chongqing University of Posts and Telecommunications
Email: miaojg@cqupt.edu.cn
Email: miaojg@cqupt.edu.cn
Download:Special Session #06.pdf
Introduction of Special Session #06
As industrial equipment evolves towards larger-scale, more complex, and intelligent systems, its operational safety and reliability face unprecedented challenges. Data-driven methods, leveraging vast amounts of operational data and advanced technologies such as machine learning and deep learning, provide innovative solutions for fault diagnosis, prediction, and health management of complex equipment.
This session aims to provide a high-level platform for domestic and international experts and scholars to share the latest research achievements, innovative technologies, and successful application cases in the field of data-driven fault diagnosis, prognosis, and health management for complex equipment. Topics of interest include, but are not limited to:
- Data-driven state monitoring methods for complex equipment
- Data-driven anomaly detection methods for complex equipment
- Machine learning-based fault diagnosis methods for complex equipment
- Deep learning-based fault prediction methods for complex equipment
- Health state assessment and remaining useful life prediction methods for complex equipment
- Data-driven intelligent maintenance and decision support for complex equipment
- Applications in aerospace, energy and power, rail transportation, and other fields
- We sincerely invite experts and scholars in related fields to actively contribute and share the latest research findings, jointly exploring the development trends and future directions of data-driven fault diagnosis, prognosis, and health management for complex equipment.
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Special Session #07
Intelligent monitoring, diagnosis and control technology for system operation safety
Session Organizers:
Zhang Ke, Professor, Chongqing University
Email: zhangke@cqu.edu.cn
Chai Yi, Professor, Chongqing University
Email: chaiyi@cqu.edu.cn
Chen Liping, Professor, Hefei University of Technology
Email: lip_chen@hfut.edu.cn
Li Huafeng, Professor, Kunming University of Science and Technology
Email: hfchina99@163.com
Wang Kunpeng, Professor, Southwest University of Science and Technology
Email: wkphnzk@163.com
Sun Jian, Associate Professor, Southwest University
Email: cqjjsunjian@swu.edu.cn
Zhu Zheren, Assistant Researcher, Hangzhou Normal University
Email: drzzr@hznu.edu.cn
Download:Special Session #07.pdf
Introduction of Special Session #07
As the core infrastructure for the operation of the national economy, the operational safety of industrial process systems is directly related to production efficiency, resource utilization efficiency, and social public safety. Such systems possess complex characteristics such as multi-physical field coupling, strong nonlinearity, spatiotemporal dynamic characteristics, and distributed collaboration. These characteristics make it difficult for traditional safety monitoring and control methods to meet the demands of modern industries. With the continuous breakthroughs in key technologies such as sensing technology and the industrial Internet, as well as the rapid iteration of big data and artificial intelligence algorithms, the field of the operational safety of industrial process systems has ushered in opportunities for innovative development. Currently, research in this field focuses on three core directions:
1.How to construct a deep knowledge mining system based on multi-source heterogeneous data and reveal the potential laws of system operation through artificial intelligence technologies;
2.How to establish an intelligent diagnostic framework that integrates mechanism models and data-driven approaches to achieve full-life cycle monitoring of system functional safety;
3.How to develop closed-loop control methods based on barrier function theory and safe reinforcement learning to build intelligent control systems with autonomous safety capabilities.
This special issue is committed to bringing together cutting-edge achievements in fields such as data science, artificial intelligence, and control theory, providing a high-end academic exchange platform for theoretical innovation and engineering applications in the monitoring, diagnosis, and control of the operational safety of industrial process systems. The issue focuses on the following research directions:
Prediction of key performance indicators and process monitoring of industrial process systems;
Data-driven fault detection, classification, and traceability;
Safety control and safe reinforcement learning oriented to the operating status of the system;
Data-driven fault detection, classification, and traceability;
Safety control and safe reinforcement learning oriented to the operating status of the system;
Special Session #08
Mechanical System Dynamic Modeling, Condition Monitoring, and Intelligent Diagnosis Technology
Session Organizers:
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Ma Ping, Associate Professor, Xinjiang UniversityEmail: yanghf20@mails.jlu.edu.cnDownload:Special Session #08.pdf
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Introduction of Special Session #08With the development of modern mechanical systems towards high precision, high complexity, and intelligentization, dynamic modeling, condition monitoring, and intelligent diagnosis technologies for mechanical systems have become crucial for ensuring their safe operation and enhancing efficiency. This special topic focuses on addressing challenges in dynamic behavior analysis, real-time condition monitoring, and accurate fault diagnosis throughout the full life cycle of mechanical systems through theoretical innovation and technological integration, thereby promoting the deep application of cutting-edge technologies such as artificial intelligence (AI) and digital twin in the field of fault diagnosis. Aimed at providing a high-level communication platform for experts and scholars at home and abroad, this special topic showcases the latest research achievements, innovative technologies, and successful application cases in the domains of system dynamic modeling, condition monitoring, and intelligent diagnosis, offering theoretical and technical support for the safe and stable operation of mechanical systems. The topics covered include but are not limited to:Dynamic modeling technology for mechanical systems;Life prediction technology for key components of mechanical systems;Artificial intelligence-based fault diagnosis technology for mechanical systems;Applications of artificial intelligence technology in condition monitoring of chemical, power, and agricultural systems.
Special Session #09
Fault Diagnosis and Safety Monitoring of Wind, Solar, Thermal and Storage Combined Power System and their Equipment
Session Organizers:This topic focuses on the key technical challenges of multi energy collaborative operation in the new power system. It will delve into the fault mechanisms, intelligent diagnostic methods, and safety monitoring technologies of wind, solar, thermal and storage combined power generation systems and their key equipment, with a focus on exchanging innovative applications of cutting-edge technologies such as artificial intelligence, big data, and digital twins in power equipment status assessment, fault warning, and health management. The special topic will cover core issues such as typical fault mode analysis of new energy units (wind turbines, photovoltaic arrays), traditional thermal power units, and energy storage systems, research and development of online monitoring technology, and optimization of life prediction models. At the same time, it will focus on the safety and protection strategies of multi energy complementary systems under extreme weather conditions, jointly promoting the breakthrough and development of intelligent operation and maintenance technology for power equipment, and providing key technical support and solutions for building a safe, efficient, and low-carbon new power system.Special Session #10
Frontier Technologies for Fault Diagnosis, Safety Control and Health Management of High-Speed Railway Traction Drive Systems
Session Organizers:Maiying Zhong, Professor, Shandong University of Science and TechnologyEmail: myzhong@sdust.edu.cnTao Peng, Professor, Central South UniversityEmail: pandtao@csu.edu.cnKaixun He, Professor, Shandong University of Science and TechnologyEmail: kaixunhe@sdust.edu.cnChao Yang, Lecturer, Central South UniversityEmail: chaoyang@csu.edu.cnDownload:Special Session #10.pdfIntroduction of Special Session #10This special topic aims to provide an international exchange platform for experts, scholars, and engineers from academia, industry, and research institutions, focusing on the field of fault diagnosis and safety control of high-speed rail traction drive systems. It will explore cutting-edge theories, innovative technologies, and engineering applications. The core issues include intelligent diagnosis methods driven by models and data, the security of cyber-physical systems, health management and fault-tolerant control, etc. It will promote interdisciplinary integration, accelerate the transformation of research results in the fields of rail transit and intelligent manufacturing, and enhance the reliability, safety, and intelligence of complex industrial systems.The conference content will be deeply discussed and exchanged around the following themes:
Intelligent diagnosis theory: including but not limited to model-driven fault diagnosis methods, dynamic system modeling and verification; Data-driven fault diagnosis: including but not limited to fault diagnosis based on deep learning, transfer learning, and statistical methods; Active fault diagnosis and real-time state estimation technology; Safety and fault-tolerant control; Fault-tolerant control and self-healing control strategy design; Fault tolerance of discrete event systems and complex networked systems; Health monitoring and prediction technology; Case studies of fault diagnosis in high-speed rail traction drive systems; Optimization of maintenance strategies for rotating machinery such as traction motors, etc.
