Special Session #01 AI-Enabled Prognostics and Health Management for Renewable Energy Systems
Special Session #04 Multivariate signal analysis and multimodal data fusion in PHM applications
Special Session #05 Model-Data Interaction for diagnostics and prognostics of Mechanical Systems
Special Session #11 AI for Industrial Fault Diagnosis and Predictive Maintenance Systems
Special Session #12 Advanced NDT methods and Intelligent Applications
Special Session #15 Digital Twin and Domain Generalization for Intelligent Manufacturing Systems
Special Session #17 Advanced Non-contact Measurement Techniques and Signal Processing Methods
AI-Enabled Prognostics and Health Management for Renewable Energy Systems
Session Organizers:
Dandan Peng, The Hong Kong Polytechnic University
Email: dandanpeng2@gmail.com
Jinpeng Tian, The Hong Kong Polytechnic University
Email: jinpeng.tian@polyu.edu.hk
Zhuyun Chen, Guangdong University of Technology
Email: mezychen@gdut.edu.cn
Download: Special Session #1.pdf
Renewable energy systems, such as wind turbines, photovoltaic (PV) systems, and energy storage systems, are critical components of the global energy transition. However, these systems often operate in complex and dynamic environments, making them susceptible to performance degradation and failures due to factors such as weather conditions, load fluctuations, and aging equipment. Traditional maintenance strategies, which are typically reactive or schedule-based, are inefficient and costly. The integration of Artificial Intelligence (AI) and Prognostics and Health Management (PHM) technologies offers a transformative approach to monitor, predict, and manage the health of renewable energy systems. By leveraging AI-driven data analytics, multi-sensor fusion, and predictive modeling, PHM enables real-time fault diagnosis, remaining useful life (RUL) prediction, and optimized maintenance strategies, thereby improving system reliability and reducing operational costs. This special session aims to bring together researchers, engineers, and industry experts to present and discuss the latest advancements in AI-enabled PHM for renewable energy systems.
The topics of interest include, but are not limited to:
• Multi-sensor data fusion
• Machine learning and deep learning algorithms for anomaly detection, fault classification, and failure prediction
• Transfer learning and domain adaptation for fault diagnosis and RUL prediction
• Digital twin technology for virtual representation and real-time monitoring
• Explainable AI (XAI) for interpretable fault diagnosis and decision-making
• Physics-informed neural networks for degradation modeling and RUL prediction
• Uncertainty quantification and risk assessment
• Fleet-level condition monitoring
• Any other related topics
Graph Representation Learning for Prognostics and Health Management in Industrial Systems: Towards Intelligent Maintenance
Session Organizers:
Jie Liu, Huazhong University of Science and Technology
Email: jie_liu@hust.edu.cn
Xingxing Jiang, Soochow University
Email: jiangxx@suda.edu.cn
Yanglong Lu, The Hong Kong University of Science and Technology
Email: maeylu@ust.hk
Zhongxu-Hu, Huazhong University of Science and Technology
Email: zhongxu_hu@hust.edu.cn
Download: Special Session #2.pdf
Prognostics and health management (PHM) has attracted significant attention within the industrial community due to its potential to enhance equipment reliability and reduce operational costs. However, several challenges remain in effectively managing existing knowledge to identify the cause-and-effect relationships between failures, particularly in establishing the correlation between identified failures and their root causes. Graph representation learning (GRL), with its focus on relationship modeling, presents a promising approach to address these challenges. By capturing and leveraging the dependencies between system components or equipment, GRL demonstrates remarkable potential in advancing PHM tasks and opens new avenues for model interpretability. Despite these advantages, the implementation of GRL varies significantly across different PHM tasks, making it challenging to develop standardized graph construction strategies for diverse scenarios. Given the current lack of unified frameworks and guidelines for applying GRL to specific PHM tasks, further investigations are needed to bridge this gap. This special session aims to foster the development of GRL for PHM applications. Therefore, we strongly encourage researchers to submit original research and review articles that contribute to this critical area of study.
Potential topics include but are not limited to the following:
Mechanism Knowledge Embedded Intelligent Mechanical Fault Diagnosis and Remaining Useful Life Prediction
Session Organizers:
Fei Jiang, Dongguan University of Technology
Email: jiangfei@dgut.edu.cn
Gang Chen, South China University of Technology
Email: gangchen@scut.edu.cn
Jipu Li, The Hong Kong Polytechnic University
Email: jipu1994.li@polyu.edu.hk
Zuozhou Pan, China Jiliang University
Email: panzz@cjlu.edu.cn
Download: Special Session #3.pdf
Modern mechanical systems often operate under complex conditions involving variable speeds, dynamic loads, and high noise levels, which obscure critical fault-related features in raw vibration signals. While data-driven approaches like deep learning have shown promise in fault diagnosis (FD) and remaining useful life (RUL) prediction, their performance is constrained by insufficient physical interpretability, poor generalization under unknown conditions, and limited integration with domain-specific mechanism knowledge. To address these challenges, embedding physical principles of mechanical degradation (e.g., fatigue dynamics, tribological interactions, and failure propagation mechanisms) into intelligent models has emerged as a transformative strategy. This Special Session focuses on methodologies that synergize mechanism-driven knowledge with advanced data analytics to enhance model robustness, interpretability, and cross-domain adaptability. Topics include physics-guided signal processing, mechanism-embedded hybrid deep learning architectures, and knowledge-informed degradation modeling, aiming to bridge the gap between theoretical mechanics and intelligent prognostics for real-world industrial applications.
The topics of interest include, but are not limited to:
• Physics-guided signal processing for fault feature extraction
• Mechanism-driven degradation modeling and RUL prediction
• Interpretable hybrid models combining physics and deep learning
• Embedding tribological/fatigue mechanisms into data-driven frameworks
• Knowledge-informed sensor fusion for multi-source data analytics
• Mechanism-aware domain adaptation under variable operating conditions
• Physics-constrained neural networks for FD and RUL prediction
• Real-time monitoring systems integrating mechanism knowledge
• Case studies on rotating machinery, bearings, gears, and aerospace systems
• Benchmark datasets and evaluation metrics for physics-aware models
Multivariate signal analysis and multimodal data fusion in PHM applications
Session Organizers:
Prof. Zong Meng, Yanshan University
Email: mzysu@ysu.edu.cn
Prof. Yuejian Chen, University of Manitoba
Email: Yuejian.Chen@umanitoba.ca
Associate Prof. Rui Yuan, Wuhan University of Science and Technology
Email: yuanrui@wust.edu.cn
Postdoctoral Associate. Hewenxuan Li, Cornell University
Email: hewenxuan.li@cornell.edu
Download: Special Session #4.pdf
Prognostic and Health Management (PHM) undergoes paradigm-shifting transitions from reactive maintenance to proactive and self-aware intelligence in industrial systems. The indispensable thrust of fusing multivariate signal analysis and multimodal data fusion will tackle challenges of modern equipment PHM. Multivariate signal analysis focuses on analyzing high-dimensional, multi-channel, and multi-sensor data. This analysis allows for the extraction of critical features that are sensitive to degradation, even in the presence of noise, interference, and changing operating conditions. By processing these signals, advanced techniques can isolate potential faults early on, providing a precise model of how the system’s condition is degrading over time. This early detection is essential for preventing unexpected failures and extending the lifespan of the equipment. Meanwhile, multimodal data fusion also plays a vital role in combining information from various types of sensors to form a comprehensive understanding of the system’s health. Multi-modal sensory inputs provide diverse insights into system health but simultaneously subjected to various uncertainties. AI-driven data fusion techniques can enhance health indicators while quantifying uncertainties that reveal equipment’s state. These capabilities underpin the transformative PHM transitions: from sensor-based diagnostics to AI-driven prognostics, scheduled to condition-based maintenance, isolated to holistic lifecycle management. This session highlights innovations bridging sensing, analytics, and action. Applications span aerospace, energy, and smart manufacturing.
The topics of interest include, but are not limited to:
Session Organizers:
Prof. Qing Ni, Northwestern Polytechnical University
Email: qing.ni@outlook.com.au
Associate Prof. Ying Zhang, University of Science and Technology Beijing
Email: ying.zhang@ustb.edu.cn
Associate Prof. Chenyang Ma, Xi’an University of Posts & Telecommunications
Email: machenyang@xupt.edu.cn
Download: Special Session #5.pdf
Mechanical systems are the backbone of critical industries, including manufacturing, energy, transportation, and aerospace. The failure of key components, such as bearings, gears, and turbines, in mechanical systems can lead to catastrophic consequences, including substantial economic losses, safety risks, and operational downtime. Traditional fault diagnosis and remaining useful life (RUL) prediction methods typically rely on either physics-based models or purely data-driven approaches, both of which with inherent limitations. Physics-based models offer interpretability and domain knowledge but may struggle to generalize across diverse and dynamic operating conditions. On the other hand, data-driven methods often require extensive labeled datasets and may lack physical consistency, limiting their reliability in real-world applications.
The integration of model-based and data-driven approaches, often referred to as model-data interaction, has emerged as a promising paradigm to overcome these limitations. By combining the interpretability and domain knowledge of physics-based models with the adaptability power of data-driven techniques, this fusion enables more accurate, robust, and interpretable fault diagnosis and RUL prediction. This session aims to explore the latest advancements, challenges, and applications of model-data interactive fault diagnosis and RUL prediction methods for key components of mechanical systems in complex and dynamic operating environments.
The topics of interest include, but are not limited to:
Prof. Hao Zhang, Hebei University of Technology
Email: zhanghao@hebut.edu.cn
Prof. Zhaozong Meng, Hebei University of Technology
Download: Special Session #6.pdf
Rotating machinery is one of the most common classes of mechanical equipment and plays an important role in industrial applications. It generally operates under tough working environment and is therefore subject to failures, which may cause machinery to break down and decrease machinery service performance such as manufacturing quality, operation safety, etc. It is therefore necessary to carry out an efficient condition monitoring for rotating machinery to increase reliability against possible faults. Traditional monitoring techniques include lubricant oil analysis, vibration and noise analysis, thermal analysis, etc. Lubricant oil analysis is generally offline and needs special instruments. Vibration and noise analysis are susceptible to the background noise. Thermal analysis is often not sensitive to the fault until reaching a serious stage.
Adopting novel smart sensing technology to acquire signals that include rich information regarding the operating condition of rotating machinery will be meaningful from the diagnostic point of view. In addition, AI-based condition monitoring approaches have become noteworthy for interpretating the acquired signals and obtaining fault signatures as early as possible. This session aims to provide a common platform for professionals, engineers, practitioners and researchers to explore their latest achievements in the field of smarting sensing technology and AI-based condition monitoring for rotating machinery.
The topics of interest include, but are not limited to:
Prof. Zhongliang Xie, Northwestern Polytechnical University
Email: zlxie@nwpu.edu.cn
Prof. Wenjun Gao, Northwestern Polytechnical University
Email: gaowenjun@nwpu.edu.cn
Associate Prof. Huihui Feng, Hohai University
Email: fenghh@hhu.edu.cn
Download: Special Session #7.pdf
The mechanical transmission system is the core power heart of large critical equipment, including large ships, aircraft engines, turbines, and so on. As the key supporting component of mechanical systems, bearings are the "joints" of the entire mechanical transmission system. Its lubrication performance, load-bearing capacity, fault diagnosis and prediction will directly affect the safe, stable and reliable operation of the entire mechanical transmission system.
However, the traditional bearings (whether plain journal bearings or rolling element bearings) have limitations in terms of load-bearing capacity, working range, vibration and noise reduction performances. Therefore, there is an urgent need to develop and propose new configurations of bearings——Intelligent bearings. In addition, the lubrication theory, fault prediction methods, and model theoretical system of intelligent bearings also urgently need to be expanded. Based on this, the topic will explore new theories of bearing lubrication, accurate prediction models for load-bearing capacity, fault diagnosis methods and prediction models, etc.
The topics of interest include, but are not limited to:
Zhilin Dong (Zhejiang Normal University)
Email: d18133679022@zjnu.edu.cn
Zijian Qiao (Ningbo University)
Download: Special Session #8.pdf
In the rapidly evolving fields of artificial intelligence and machine learning, domain adversarial, domain adaptation, domain alignment, and domain generalization technologies have emerged as critical research directions for enhancing model robustness and generalization capabilities.
• Domain adversarial techniques leverage adversarial training to improve model robustness against data distribution discrepancies.
• Domain adaptation addresses distribution shifts between source and target domains to enable cross-domain transfer of models.
• Domain alignment reduces domain gaps by aligning feature spaces across source and target domains.
• Domain generalization focuses on building models capable of generalizing to unseen domains, thereby enhancing their adaptability.
These technologies hold immense potential across diverse fields, including but not limited to fault diagnosis. To advance their development and application, we hereby launch this special topic collection, inviting contributions that showcase cutting-edge research achievements, innovative methodologies, and practical insights.
Associate Prof. Xiaoan Yan, Nanjing Forestry University
Email: yanxiaoan@njfu.edu.cn
Prof. Yifan Li, Southwest Jiaotong University
Email: liyifan@swjtu.edu.cn
Associate Prof. Pengfei Liang, Yanshan University
Email: liangpf@ysu.edu.cn
Dr. Ruyi Huang, South China University of Technology
Email: snowxiaoyu@hotmail.com
Download: Special Session #9.pdf
Rotating mechanical (such as motors, fans, gearboxes, aircraft engines, etc.) is the core equipment in the fields of electricity, energy, manufacturing, transportation, etc. Its operating status directly affects production safety and economic benefits. However, in complex industrial scenarios, equipment often faces challenges such as variable operating conditions, strong noise, multi-source interference, and data scarcity. Traditional diagnostic methods are difficult to meet the high-precision and real-time operational requirements. With the development of artificial intelligence, Internet of Things (IoT) and edge computing technology, intelligent diagnosis and predictive maintenance provide a new solution for the health management of rotating machinery. This topic aims to gather experts from academia and industry to explore the latest developments, key challenges, and practical implementation of intelligent diagnosis and prediction technology in multiple scenarios, and promote the intelligent upgrading of the industry.
The topics for interest include, but are not limited to:
• Intelligent diagnosis technology under complex working conditions
• Remaining life prediction technology under complex working conditions
• Multi-scale and multi-signal domain information fusion technology
• Multi-modal data fusion technology
• Mechanism and data fusion driven technology
• Cross scene domain generalization and domain adaptation techniques
• Multi-task learning technology for fault diagnosis and prediction
• Digital twin technology for fault diagnosis and prediction
• Industrial applications and case sharing
Novel Sensing/Transduction Technologies: Principles, Mechanisms, Signal Processing and Multi-physics Analysis
Min Xue, Chang’an University
Email: xmgrace0825@163.com
Jing Ji, Xidian University
Email: jingji@xidian.edu.cn
Yun Zhang, Xidian University
Email: yunzhang@xidian.edu.cn
Download: Special Session #10.pdf
Novel sensor technologies and advanced radar signal processing methods have jointly driven the rapid development of intelligent sensing systems, which find wide applications in human health monitoring, radar imaging and target recognition. MEMS, flexible, energy-harvesting, and multi-modal sensors have significantly improved the accuracy and sensitivity of data acquisition. SAR imaging and target detection technologies have enhanced the recognition and imaging capabilities of radar systems. Through the combination of multi-sensor fusion and intelligent algorithms, more efficient and accurate intelligent sensing is realized. This session focuses on the latest developments, challenges, and applications of novel sensors, signal processing techniques, and their integration for intelligent sensing across various domains.
The topics of interest include, but are not limited to:
• Novel sensor technologies, including MEMS, flexible, bio-inspired, energy-harvesting, and multi-modal sensors.
• Advanced radar signal processing methods such as SAR imaging and moving target detection.
• Multi-sensor data fusion and intelligent algorithms for enhanced perception.
• Applications of novel sensors and signal processing in human health monitoring and environmental sensing.
• Novel sensing/transduction principles and mechanisms
• Sensing/transduction technology for new energy and energy storage systems
• Multi-field coupling analysis in novel sensing/transduction technologies
• Any other related topics.
AI for Industrial Fault Diagnosis and Predictive Maintenance Systems
Prof. Jianyu Long, Dongguan University of Technology.
Email: longjy@dgut.edu.cn
Prof. Deqiang He, Guangxi University.
Email: hdqlqy@gxu.edu.cn
Prof. Fengtao Wang, Shantou University.
Email: ftwang@stu.edu.cn
Download: Special Session #11.pdf
Industrial machinery and equipment are critical to modern manufacturing, energy production, and transportation systems. However, they often operate under harsh conditions, leading to wear, degradation, and unexpected failures. Traditional fault diagnosis and maintenance approaches, such as vibration analysis, thermal monitoring, and manual inspections, face challenges in accuracy, real-time capability, and scalability.
Recent advances in artificial intelligence (AI) and smart sensing technologies have revolutionized industrial condition monitoring, enabling early fault detection, intelligent diagnosis, and predictive maintenance (PdM). AI-driven approaches, including deep learning, reinforcement learning, and federated learning, can process multi-modal sensor data (vibration, acoustic emission, thermal, current, etc.) to extract meaningful fault signatures and predict remaining useful life (RUL). These technologies enhance equipment reliability, reduce downtime, and optimize maintenance costs in smart factories and Industry 4.0 applications.
This Special Session aims to bring together researchers, engineers, and industry experts to discuss cutting-edge AI methods for industrial fault diagnosis and predictive maintenance systems. We welcome contributions on novel algorithms, real-world case studies, and emerging challenges in this rapidly evolving field.
The topics of interest include, but are not limited to:
Advanced NDT methods and Intelligent Applications
Session Organizers:
Yafei Xu, Harbin Institute of Technology
E-mail: yafeixu@hit.edu.cn
Liuyang Zhang, Xi’an Jiaotong University
E-mail: liuyangzhang@xjtu.edu.cn
Download: Special Session #12.pdf
Non-destructive testing (NDT), as a non-invasive characterization technology for assessing structural integrity, has gained great attention in various industrial fields, including the aerospace, wind turbine blade, nuclear equipment, and so on. Currently, many advanced NDT methods, algorithms, and systems have been proposed for the service reliability and operational safety of equipment. Specially, with the rapid development of artificial intelligence technique, its powerful data processing capabilities will provide more possibilities and insights in NDT field. This special session aims to focus on new technologies, new principles, new methods, and new applications in NDT, and share the latest research developments and innovative ideas.
The topics of interest include, but are not limited to:
Advanced Sensing, Data Processing and Big Data Analytics for New Type Power Systems
Session Organizers：
Chair：
Associate Prof. Min Zhang, Northwest University, E-mail: dr.zhangmin@nwu.edu.cn
Co-Chair：
Senior Engineer, Wei Deng, Xi’an Thermal Power Research Institute Co.,Ltd., E-mail: dengwei@tpri.com.cn
Associate Prof. Jiang Wu, Xi'an Polytechnic University, E-mail: wujiang@xpu.edu.cn
Associate Prof. Chi Chen, Xi’an University of Technology, E-mail: chenchi@xaut.edu.cn
Associate Prof. Shuang Wang, Xi’an Jiaotong University, E-mail: shuang@xjtu.edu.cn
Download: Special Session #13.pdf
With the deepening of clean and low-carbon transformation of energy, large-scale development and utilization of renewable energy, distributed energy, and energy storage are developing rapidly. Digital technology is used to empower the traditional power grid, continuously improve the IntelliSense ability, interaction level, and operation efficiency of the power grid, effectively support various energy access and comprehensive utilization, and continuously improve energy efficiency. Based on the research of new sensing, monitoring, and diagnosis technology, the modern information technology and advanced communication technology, such as mobile Internet and artificial intelligence, are fully applied to realize the comprehensive condition IntelliSense, efficient information processing, convenient and flexible application for key equipment in smart grid, such as transformers, switches, cables, power transmission lines, wind turbines, solar photovoltaic systems. This session is intended to focus on the intellisense, monitoring, and diagnosis in the new type power system.
Research interests include but are not limited to:
• Intellisense technology and its application for power equipment and renewable energy conversion system
• Conditioning monitoring and diagnosis for transformer, GIS, cable, power transmission line, wind turbine, photovoltaic system, etc.
• Related research on edge computing, artificial intelligence, big data and data processing
Principles, methods and applications of photoelectron spectroscopy analytical instruments
Session Organizers：
Chair：
Prof. Yu Chen, Xi’an Jiaotong University, E-mail: chenyu@xjtu.edu.cn
Co-Chair：
Prof. Shulin Liu, Institute of High Energy Physics, Chinese Academy of Science, E-mail: liusl@ihep.ac.cn
Prof. Yu Huang, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, E-mail: ssshycn@163.com
Prof. Zicai Shen, Beijing Institute of Spacecraft Environment Engineering, China Academy of Space Technology, E-mail: zicaishen@163.com
Download: Special Session #14.pdf
As the demand for high-performance materials in critical fields such as aerospace, semiconductor chips, and photovoltaics continues to grow, the importance of material surface processing is also increasing. Photoelectron Spectroscopy instrument and analysis techniques, such as PYS, UPS, XPS, IPES, AES, etc., play a significant role in understanding the surface chemical properties of materials, investigating surface modifications, and developing new devices. This special session aims to bring together experts and scholars in the research and application of photoelectron spectroscopy instruments to discuss the latest progress of different photoelectron spectroscopy instruments and their applications in various fields.
Research interests include but are not limited to:
• Photoelectron spectroscopy instrument technology (PYS, XPS, UPS, IPES, AES, SEY, etc.)
• Key components of photoelectron spectroscopy instrument
• Photoelectron spectroscopy analysis technology
• Surface characteristic analysis method
• Theoretical modeling and calculation in photoelectron spectroscopy field
• Applications of photoelectron spectroscopy instrument technology
Digital Twin and Domain Generalization for Intelligent Manufacturing Systems
With the rapid advancement of Industry 4.0, digital twins (DTs) are becoming a cornerstone of intelligent manufacturing by providing virtual replicas of physical assets and processes for real-time monitoring, fault diagnosis, and performance optimization. At the same time, a key challenge for artificial intelligence (AI) in industrial systems lies in its limited ability to generalize across diverse operating conditions and unseen environments. Domain generalization and adaptation techniques address this issue by enabling robust, transferable, and reliable learning under distribution shifts.
Integrating digital twins with domain generalization approaches establishes a powerful paradigm for developing resilient, interpretable, and adaptive AI-driven solutions. Digital twins can generate diverse synthetic data and simulate operational variability, while domain generalization methods extract invariant representations that enhance adaptability to real-world scenarios.
This session aims to explore the synergies between DTs, AI, and domain generalization for advancing fault diagnosis, predictive maintenance, and intelligent decision-making in applications ranging from smart factories and robotics to aerospace and energy systems.
Session Organizers:
Prof. Lingli Jiang, Foshan University, Email: linlyjiang@163.com
Dr. Ziqiang Pu, Chongqing Technology and Business University, Email: puziqiang@ctbu.edu.cn
Dr. Yunwei Huang, Dongguan University of Technology, Email: huangyunwei@dgut.edu.cn
Prof. Xiaochuan Li, Hefei University of Technology, Email: xiaochuan.li@hfut.edu.cn
Download: Special Session #15.pdf
The topics of interest include, but are not limited to:
Advanced Artificial Intelligence and Industrial Large Models for Complex Industrial Systems
Session Organizer:
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.
Liyuan Ren, Institute of Applied Physics and Computational Mathematics, Email: ren_liyuan@iapcm.ac.cn
Liyuan Ren was born in Xichang, Sichuan, China. He received a B.Eng. degree in electronic information engineering from the UESTC, Chengdu, China, and an Eng. (Hons.) degree in electrical and electronics engineering from the University of Glasgow, Glasgow, U.K., in 2020. He received an M.Sc. degree in signal and systems with Delft University of Technology (TU Delft), Delft, the Netherlands, in 2022. He is currently an Assistant Researcher in the Institute of Applied Physics and Computational Mathematics, Beijing, China. Ren’s research interests include nondestructive tests, reliability analysis and multi-sensor navigation.
Jiayu Chen, Nanjing University of Aeronautics and Astronautics, Email:jiayu_chen@nuaa.edu.cn
Jiayu Chen received the B.S. degree in transportation engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2013, and the Ph.D. degree in system engineering from Beihang University, Beijing, China, in 2019. Currently, he is an Associate Professor with the College of Civil Aviation and the Civil Aviation Key Laboratory of Aircraft Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics. In 2023, he obtained Hong Kong Scholars Award and conducted Post-Doctoral Research with the City University of Hong Kong, Hong Kong. His research interests include prognostics and health management and intelligent fault diagnosis for complex aircraft and aerospace systems.
Dun Li, Tsinghua University, Email: lidun@tsinghua.com
Dun Li is currently a postdoctoral researcher at the Department of Industrial Engineering, Tsinghua University. He obtained his Ph.D. in Computer Science and Technology from Institut Polytechnique de Paris (Telecom SudParis) in 2025, and a Ph.D. in Information Management and Information Systems from Shanghai Maritime University in 2023. His research focuses on artificial intelligence, large-scale models, and the Industrial Internet of Things, with particular emphasis on fault diagnosis, multimodal data fusion, and intelligent operation and maintenance. As first or corresponding author, he has published more than ten papers in SCI-indexed journals such as IEEE Transactions on Sustainable Computing and Computer Communications, with over 1,500 citations. He has participated in several national and provincial research projects and received multiple prestigious awards, including the Third Prize of the China Invention Association Invention and Entrepreneurship Award, the Gold Medal at the 28th National Invention Exhibition, and the Gold Medal at the Geneva International Exhibition of Inventions. He also led the project Sky Eye Navigation, which was selected for the global exhibition Prototypes for Humanity in Dubai.
Session Introduction:
As industrial systems grow increasingly complex (e.g., aerospace equipment, high-speed trains, intelligent manufacturing, smart energy networks), traditional methods for reliability, maintainability, and safety assurance struggle with high-dimensional coupling, dynamic evolution, and multi-source heterogeneous data. Emerging Industrial Large Models (ILMs) are opening transformative pathways for lifecycle quality control. By integrating domain knowledge with multimodal industrial data (sensor time-series, maintenance logs, 3D models, etc.), ILMs demonstrate disruptive capabilities in panoramic system perception, collaborative fault prediction, and autonomous risk decision-making.
This research focuses on ILM-centered hybrid frameworks that combine AI with mechanism models, digital twins, and edge intelligence, ensuring physical consistency and enabling a shift from “experience-driven” to “data-knowledge dual-driven” reliability prediction and real-time decision-making. Pre-training and domain adaptation tailored for industrial scenarios further address cross-device and cross-scenario generalization. Ultimately, the goal is to establish a comprehensive industrial cognitive intelligence infrastructure, spanning edge to cloud, with ILMs as the core engine for predictive accuracy and autonomous decision-making in complex environments.
Download: Special Session #16.pdf
Advanced Non-contact Measurement Techniques and Signal Processing Methods
Session Organizer:
Prof. Yuyong Xiong, Shanghai Jiao Tong University.
Email: yy.xiong@sjtu.edu.cn
Prof. Wei Fan, Jiangsu University
Email: weifan@ujs.edu.cn
Prof. Peng Zhou, Tongji University
Email: zhoupengzjsx@tongji.edu.cn
Session Introduction:
Non-contact measurement technologies are playing an increasingly important role in modern science and engineering by enabling accurate, rapid, and non-invasive data acquisition. Emerging methods such as laser Doppler vibrometry, digital holography, terahertz and millimeter-wave sensing, infrared thermography, and air-coupled ultrasonics are expanding capabilities for structural health monitoring, fault diagnostics, industrial inspection, and robotics. These approaches are particularly attractive in situations where traditional contact-based sensors are either impractical or intrusive, offering new opportunities for real-time monitoring, higher reliability, and safer operation.
In parallel, advances in signal processing are transforming how data from non-contact sensors are analyzed and applied. Techniques including time–frequency analysis, wavelet transforms, compressive sensing, and Bayesian filtering are increasingly combined with machine learning, deep learning, and multimodal data fusion to extract hidden features, suppress noise, detect anomalies, and enable predictive decision-making. The synergy between advanced non-contact measurement and intelligent signal processing is paving the way for next-generation monitoring and diagnostic systems across aerospace, civil infrastructure, manufacturing, and healthcare.
This special session will provide a focused forum for researchers, practitioners, and industry experts to present recent developments, share practical experiences, and explore future directions in the field. We invite contributions that highlight novel sensing methods, innovative signal processing algorithms, and real-world applications that demonstrate the transformative potential of advanced non-contact measurement.
Download: Special Session #17.pdf
The topics of interest include, but are not limited to:
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