Conference contact:
Wang Hang 13966759716;
Cao Zheng 15256597661;
Wu Jianpeng 15811319103;
Liu Xianzeng 15620559510.
Special Session #01 Artificial self-recovery of high-end mechanical equipment
Special Session #02 Condition monitoring and fault diagnosis for rotating machinery blade
Special Session #03 Numerical Model Driving Personalized Diagnosis for Fault Detection / Condition Monitoring
Special Session #04 Remaining Useful Life (RUL) Prediction of Rotating Machinery
Special Session #05 Intelligent operation and maintenance technology for aerospace equipment
Special Session #06 Explainable deep learning for intelligent maintenance
Special Session #07 Intelligent state monitoring, diagnosis and prognosis for aeronautics and astronautics equipment
Special Session #08 Special Session on Structural Health Monitoring and Damage Detection
Special Session #09 Aero Engine MRO (Maintenance, Repair, and Overhaul) Decision Making Based on Digital Twins
Special Session #10 Technologies of Fault Diagnosis and Accommodation for Aero Engine
Special Session #11 Advancements & Applications on Aero-EngineGround Gas-Turbine Health Management Solutions and System Developments
Special Session #12 Intelligent Vehicle Equipment Fault Diagnosis and Operation and Maintenance Technology
Special Session #13 Degradation-based reliability analysis and maintenance planning
Special Session #14 Safety Risk Monitoring and Early Warning
Special Session #15 Advanced technologies of reliability and state assessment for new energy storage systems
Special Session #16 Dynamic Mechanism, Fault Diagnosis, and Remaining Useful Life Prediction of Machinery
Special Session #17 Condition monitoring, fault diagnosis and intelligent maintenance of wind energy equipment
Special Session #18 Special Session on Spare parts & Logistics
Special Session #19 Special Session on Digital twin and modeling of rotary machines
Special Session #20 Diagnosis and operation and maintenance of rail transit
Special Session #21 Deep Transfer Learning and Meta Learning Towards Fault Diagnosis and Prognosis of Machine Systems
Special Session #1
Artificial self-recovery of high-end mechanical equipment
Session Organizers:
Download: Special Session #1.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:
Special Session #2
Condition monitoring and fault diagnosis for rotating machinery blade
Session Organizers:
Download: Special Session #2.pdf
With the fast development of the fluid machinery equipment, such as centrifugal compressor, axial compressor, aeroengine, gas turbine, etc., the mission of keeping blades working in normal condition and providing early warning are becoming more and more challenging, which brings higher requirements on condition monitoring system’s operation. Therefore, there is an urgent demand to develop advanced and efficient condition monitoring, diagnosis and prognosis methods to ensure the safety of rotating machinery blades.
In particular, the blade tip-timing is urgent to make breakthroughs in condition monitoring theories, fault diagnosis and prognosis methods, signal on-line processing, early warning and comprehensive applications.
In this section, we aim to provide a forum for colleagues to report the most up-to-date research in condition monitoring, prognosis and diagnosis for rotating machinery blade，as well as comprehensive surveys of the state-of-the-art in relevant specific areas. 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:
Special Session #3
Numerical Model Driving Personalized Diagnosis for Fault Detection / Condition Monitoring
Session Organizers:
Prof. Jiawei Xiang
Special Session #4
Remaining Useful Life (RUL) Prediction of Rotating Machinery
Session Organizers:
Prof. Ke Li
Dr. Ke Li received the Ph.D. in mechatronic engineering from Mie University, Japan, in 2012. Currently, he is a professor in the College of Mechanical Engineering, Jiangnan University, Wuxi, 214122, China. His research interests include Vibration signal processing, Rotation machinery fault diagnosis, Structural health monitoring and Visual inspection. He published more than 100 peer-reviewed international journal papers.
Email: like@jiangnan.edu.cn
Assoc. Prof. Liuyang Song
Dr Liuyang Song received the Ph.D. degree from Mie University, Tsu, Japan, in 2017. She is currently an associate 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
Download: Special Session #4.pdf
The remaining useful life (RUL) prediction has attracted substantial attention recently due to its importance for the Prognostic and Health Management. The topic of this special session is Remaining Useful Life (RUL) Prediction method for Rotating Machinery. It aims at providing a platform for experts and scholars to discuss advanced technology and applications of Remaining Useful Life (RUL) Prediction in Rotating Machinery. Submissions related to the advanced and emerging technologies and their applications in Remaining Useful Life (RUL) Prediction studies are encouraged.
The topics of interest include, but are not limited to:
Yunxia Chen, Ph.D., professor in the School of Reliability and Systems Engineering, Beihang University. Her main research interests include reliability design and experiment technology, integrating design for performance and reliability, fault diagnosis and prognostics, etc. She has published over 100 journal and international conference papers, and some research is published in top journals in her field, such as RESS, MSSP, AE, etc. She also holds more than 30 invention patents and 8 registered software copyrights in China. As a leading member, she has finished more than 20 projects, which are funded by the National Natural Science Foundation of China, the National Key Research and Development Plan, and some dominating enterprises in her field. She has won the first prize for the National Defense Science and Technology Progress, second prize for Technological Invention of the China Electronics Society, Beijing Science and Technology award.
E-mail: chenyunxia@buaa.edu.cn
Hailin Wang, deputy director of the Aerospace Science and Industry Defense Technology Research and Test Center, executive deputy director of the Quality and Reliability Technology Center of Aerospace Science and Industry Corporation, vice president of the Intelligent Manufacturing and Major Equipment Testing Branch of the China Society of Inspection and Testing, executive member of the Intelligent Operation and Maintenance Branch of the Chinese Mechanical Engineering Society, and director of the China Association for Quality. He has been engaged in quality assurance, quality reliability, comprehensive management support and technical research for aerospace and military products. He has undertaken many major research projects from the State Administration of Science, Technology, and Industry for National Defense, the military, and the Aerospace Science and Industry Corporation, and has led the development of many industry standards. He has won the first prize for Innovative Achievements in National Defense Technology and Industrial Management, and the Management Science Award of the China Management Science Society.
E-mail: 3239640@qq.com
Download: Special Session #5.pdf
Aerospace equipment is an important symbol of modern comprehensive science and technology, and it is of great significance for national security and development. However, aerospace equipment generally operates in very harsh space environments, and their functions and structures are very complex and precise. These factors may inevitably lead to various failures of the aerospace equipment, affecting its normal operation and task execution. Therefore, it has been an urgent demand to develop intelligent operation and maintenance technologies for aerospace equipment to ensure its high reliability and long lifetime.
In this section, we aim to provide a forum for colleagues to report the advanced research and comprehensive surveys concerning aerospace equipment state monitoring, fault diagnosis and prognostics, operation and maintenance optimization and decision-making, etc. Both novel theoretical contributions and practical solutions to specific problems are included in this forum.
The topics of interest include, but are not limited to:
Special Session #6
Explainable deep learning for intelligent maintenance
Session Organizers:
Ruqiang Yan, Xi’an Jiaotong University
Ruqiang Yan is a Full Professor and Director of International Machinery Center at the School of Mechanical Engineering, Xi’an Jiaotong University, China. His research interests include energy-efficient sensing, data analytics, and artificial intelligence for the condition monitoring, fault diagnosis and prognosis of large-scale, complex, dynamical systems.
Dr. Yan is a Fellow of IEEE (2022) and ASME (2019). His honors and awards include the IEEE Instrumentation and Measurement Society Distinguished Service Award in 2022, and Technical Award in 2019, the New Century Excellent Talents in University Award from the Ministry of Education in China in 2009, and multiple best paper awards. Dr. Yan is the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement, an Associate Editor of the IEEE Sensors Journal, and Editorial Board Member of several other journals.
E-mail: yanruqiang@xjtu.edu.cn
Shibin Wang, Xi’an Jiaotong University
Shibin Wang is a Professor in the School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China, where he received the Ph.D. degree in mechanical engineering in 2015. His research interests include time-frequency analysis, sparsity-assisted signal processing, interpretable neural networks for machine condition monitoring and fault diagnosis. In 2017, he was a Visiting Scholar with the Tandon School of Engineering, New York University, NY, USA. Previously, he received the B.S. and M.S. degrees in electrical engineering from Soochow University, Suzhou, China, in 2008 and 2011, respectively. Dr. Wang’s honors and awards include the Excellent Young Scholars of NSFC (2021), the First Prize of Natural Science of the Ministry of Education, China (2020), Hiwin Doctoral Dissertation Award (Silver Award, 2016), etc. He currently serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement.
E-mail: wangshibin2008@xjtu.edu.cn
Download: Special Session #6.pdf
Intelligent maintenance of industrial equipment has attracted increasing attention in both academic and industrial communities, and it is an important function in prognostics and health management (PHM). Dynamical changes in industrial equipment have to be captured in time for safe and reliable operations. These tasks are typically realized by using measurement technologies in combination with data analytics algorithms. Deep neural networks provide unprecedented performance gains in data analysis, especially in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. Recent advances in the theory and methodology about explainable deep learning have provided viable tools for dealing with the issue of intelligent maintenance of industrial equipment. This special session is seeking papers on recent research, development and applications in explainable deep learning for intelligent maintenance of industrial equipment with theoretical and/or applied nature.
Suitable topics for this special session include but are not limited to:
Yuchen Song, Ph.D., assistant professor in the School of Electronics and Information Engineering, Harbin Institute of Technology. His research interests includes lithium-ion battery cell/pack experiment and modeling, state estimation and health management of unmanned system. He is now focusing on state estimation of high reliable, long-life spacecraft battery pack, state diagnosis and prognosis for complex system, intelligent state monitoring method. He has 14 published journal and international conference paper and cited more than 800 times on Google Scholar. He also published his results in top journal in the field, such as Applied Energy, IEEE Trans. Industrial Electronics. As a leading member, he has participated in 6 projects, which are funded by National Natural Science Foundation of China, Science and Technology Foundation for National Defense, Fundamental Technology Research Projects and some dominating enterprises in his field. He also holds 3 invention patents and 5 registered software copyrights in China.
E-mail: songyuchen@hit.edu.cn
Xiaoxuan Jiao, Ph.D., Associate Professor in the School of Aviation Engineering, Air Force Engineering University. His research interests include fault diagnosis, remaining useful life prediction and health management of complex electromechanical equipment. As the person in charge, he has led 5 projects, which are funded by Basic Research Project Group, Natural Science Foundation of Shaanxi Province, etc. As the main contributor, he participated in 10 scientific research projects and has won the second prize for provincial Science and Technology Progress Award twice. He has 32 published journal and international conference paper. He also holds 4 invention patents and 6 registered software copyrights in China.
E-mail: jiaoxx_sensor@outlook.com
Pang Jingyue, Ph.D., 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．She has published more than ten journal and conference papers. As a leading member, she has participated in 5 projects, which are funded by National Natural Science Foundation of China, Chongqing Science and Technology Commission, and Chongqing Education Commission, etc.
E-mail: jypang2019@ctbu.edu.cn
Download: Special Session #7.pdf
With the fast development of the aeronautics and astronautics equipment, such as unmanned aerial vehicles, satellites, etc., the mission complexity and operating conditions are becomes more and more challenging, which brings higher requirements on system’s running safety and working reliability. Therefore, there is an urgent demand to develop advanced and efficient state monitoring, diagnosis and prognosis methods to ensure the safety of aeronautics and astronautics equipment.In particular, the time varying operating conditions and dynamic missions is urgent to make breakthroughs in state monitoring theories, state diagnosis and prognosis methods, data on-line processing, computing structure design and comprehensive applications.
In this section, we aim to provide a forum for colleagues to report the most up-to-date research in state monitoring, prognosis and diagnosis for complex aeronautics and astronautics equipment, sub-system and component， as well as comprehensive surveys of the state-of-the-art in relevant specific areas. 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:
• Advanced state monitoring principles and method for complex aeronautics and astronautics equipment
• Advanced sensing techniques for aeronautics and astronautics equipment state monitoring
• Novel diagnosis techniques and measurement systems
• Self-adaptive or domain-adaptive state diagnosis and prognosis methods
• Data acquisition and processing algorithms for state diagnosis and prognosis
• Application cases of state diagnosis and prognosis for aeronautics and astronautics equipment system, sub-system and component
• Software and hardware design for on-line state monitoring, diagnosis and prognosis
Special Session #8
Special Session on Structural Health Monitoring and Damage Detection
Session Organizers:
Jingjing He, Beihang University: Dr Jingjing He is an Associate Professor affiliated with the School of Reliability and Systems Engineering at Beihang University, Beijing, China. She received her PhD in Civil Engineering at Clarkson University, NY US. Prior to this, she received her MS and BEng from Beihang University. She was a visiting scholar in the department of Civil and Engineering Mechanics at Columbia University. Her research is focused on structural health monitoring using ultrasonics, quantitative non-destructive evaluations, smart materials and structures, fatigue and fracture of engineering materials and structures. She serves as an associate editor of ASME Journal of NDE.
E-mail:hejingjing@buaa.edu.cn
Xun Wang, Beihang University: Dr. Xun Wang received his PhD in "Estimation of multiple sound sources with data and model uncertainties" in Dec 2014 from the Sorbonne University - University of Technology of Compiègne, France. Between Mar 2015 and Sept 2016, he was a postdoctoral fellow at the Aix-Marseille University, France. Between Oct 2016 and Feb 2020, he was a Research Associate at the Hong Kong University of Science and Technology. He joined in the School of Reliability and Systems Engineering, Beihang University, China in Apr 2020 and have been a Full Professor since Feb 2022. His research interests include acoustical inverse problems, structural health monitoring, acoustic imaging, defect detection, and uncertainty quantification.
E-mail:xunwang@buaa.edu.cn
Download: Special Session #8.pdf
Structural health monitoring (SHM) is widely used in engineering structures, which allows condition-based maintenance to reduce the repair cost and increase the safety of structure. This special session focuses on enhancing safety, efficiency, availability and effectiveness of systems through monitoring, predicting and managing the health status of complex engineering. Academics working on the following topics (not limited to) are very much welcomed.
Special Session #9
Aero Engine MRO (Maintenance, Repair, and Overhaul) Decision Making Based on Digital Twins
Session Organizers:
Jinrui MA, AECC Commercial Aircraft Engine CO., LTD, Dr. Jinrui MA received the Ph.D. in the domain of Optimization and Security of System from University of Technology of Troyes in 2019. Currently, she is a senior engineer in AECC Commercial Aircraft Engine CO., LTD, Shanghai, 200241 China. Her research interests include PHM of aero-engine, performance deterioration modelling for complex systems.
E-mail: ma.jinrui@outlook.com
Download: Special Session #9.pdf
Today with the proliferation of IoT sensors, faster computing power, and capturing data has grown exponentially and is enabling the further development and application of integrated system model with the digital twin. According to the different scenarios, digital twins are classified into several types. Support and service digital twin is the one applied during the product service phase. Support and service digital twin could create the maximum business value by quantifying knowledge about the condition of the equipment, enhancing operational performance (including autonomous control), providing prognostics for life extension, extracting user preferences, even creating knowledge for the next product.
The goal of this special session is to promote and collect latest original research and practical contributions on support and service digital twin including both methodological and practical research.
Suitable topics for this special session include but are not limited to:
Special Session #10
Technologies of Fault Diagnosis and Accommodation for Aero Engine
Session Organizers:
Rongrong QIAN, AECC Commercial Aircraft Engine CO., LTD,Dr. Rongrong Qian received the Ph.D. in control science and engineering from University of Science and Technology of China, in 2016. Currently, she is a senior engineer in AECC Commercial Aircraft Engine CO., LTD, Shanghai, 200241, China. Her research interests include air engine control law, fault diagnostic and active fault tolerant control, distributed control system, electro-mechanical system control.
E-mail: rongrong_qian@126.com
Aiming at the developing intelligent aero-engines, researches have been carried out on intelligent distributed control technology for engines, including distributed network bus technology, intelligent sensor system and actuator system development. Fault diagnosis and accommodation technologies enhance the aero-engine reliability and flight safety, therefore are important research and development field urgently needs resources and efforts. The goal of this special section is to improve the operational performance and economy of aero engines, focusing on the following research topics:
Special Session #11
Advancements & Applications on Aero-EngineGround Gas-Turbine Health Management Solutions and System Developments
Session Organizers:
Fang Wei, AECC Commercial Aircraft Engine CO., LTD, fweiwf@126.com Fang Wei received the MS in College of Energy and Power Engineering from Nanjing University of Aeronautics and Astronautics, in 2008. Currently, she is a senior engineer in AECC Commercial Aircraft Engine CO., LTD, Shanghai, 200241, China. Her research interests include PHM Verification and Validation（V&V）, PHM Evaluation, and fault diagnostic.
E-mail:fweiwf@126.com
Download: Special Session #11.pdf
Aero-engines and Gas-turbines are complex systems working under harsh operating conditions with long service live requirements, and the failure & degradation frequencies at component level can be significant. With the continuous improvements on the system integration and comprehensive performance，as well as progress on machinery intelligence, Condition-based Maintenance (CBM) has truly delivered for the airline & power industry, and become critical for overall system operation performance of aero-engines and ground gas turbines. CBM relies on accurate engine health condition evaluation and failure prediction to support fleet MRO (maintenance, repair, and overhaul) decision. Therefore, CBM has been and continuously being an important research field for continuous improvements on the safety、reliability、and economy of aero-engines and gas-turbines. The goal of this special section is to improve the safety,reliability and economy of aero engines & ground gas turbines through research advancements in CBM, including but not limited to engine and ground gas turbine CBM system development, engine gas path diagnostics, mechanical health monitoring, engine operation (dispatch and deployment), MRO, and methodology development and application of intelligent diagnosis.
Suitable topics for this special session include but are not limited to:
Special Session #12
Intelligent Vehicle Equipment Fault Diagnosis and Operation and Maintenance Technology
Session Organizers:
Prof. Heyan Li
Prof. Li received his PhD degree at Beijing Institute of Technology (BIT) in March 2004, and then joined the School of Mechanical and Vehicle Engineering of BIT. In 2018, he was appointed as a full professor in the School of Urban Transportation and Logistics of Shenzhen University of Technology (SZTU). His main research interests include new energy vehicle drive and transmission, intelligent networked vehicles, vehicle transmission system integrated design and clutch tribology field, presiding over more than 10 completed and under research projects, and has published more than 140 peer-reviewed academic papers. He has won the Second Prize of National Science and Technology Progress once and the First Prize of National Defense Science and Technology Progress three times.
Email: liheyan@sztu.edu.cn
Associated Prof. Cenbo Xiong
Associated Prof. Xiong received his PhD degree at Beijing Institute of Technology (BIT) in June 2016, and then worked as Post-Doctor in Tsinghua University. In 2020, he joined the School of Mechanical and Vehicle Engineering of BIT and was appointed as an associate professor. His main research field is science and technology of intelligent power chain system, including design and optimization of intelligent power chain system, basic knowledge and PHM of vehicle systems and so on. Supported by more than 10 research projects, he had published more than 30 peer-reviewed papers and several patents.
Email: xiongcenbo@bit.edu.cn and xiongyi1895@163.com
Associated Prof. Jianpeng Wu
Associated Prof. Wu received the PhD degrees from Beijing Institute of Technology, Beijing, China in July 2020. He is currently an associate professor in the Ministry of Education Key Laboratory of Modem Measurement and Control Technology, Beijing Information Science & Technology University. His current research interests include intelligent information fusion， friction of vehicle transmission components, fault diagnosis and performance evaluation of transmission system. He has presided over 5 completed and under research projects, and has published more than 20 peer-reviewed academic papers. He received the National Natural Science Foundation of China in 2022.
Email: 15811319103@163.com
Download: Special Session #12.pdf
With the rapid development of intelligent vehicle equipment, improving their reliability and security has become one of the key issues that the industry must address. The failure of any one of these systems or components such as the powertrain, braking system, safety control system, etc. can lead to unstable vehicle operation and thus pose some safety threats to the passengers.
How to discover and solve vehicle faults quickly through big data remote monitoring, fault model online diagnosis, artificial intelligence technology reasoning prediction and other methods to improve the reliability and safety of vehicles, is the bottleneck of current intelligent vehicle fault diagnosis, operation and maintenance technologies. In addition, how to obtain a bulk of fault samples from the operation of actual vehicles and improve the accuracy of fault diagnosis through self-driven inference of more general fault models by artificial intelligence methods is also a pressing issue in engineering applications. The theme aims to highlight the latest advances in the field, while highlighting important directions and new possibilities for future investigations.
The topics of interest include, but are not limited to：
Special Session #13
Degradation-based reliability analysis and maintenance planning
Session Organizers:
Xiaobing Ma, Beihang University.
Xiaobing Ma is now a full professor and associate dean with the School of Reliability and Systems Engineering, Beihang University. He received his bachelor/doctor degree from Beihang University in 2001 and 2006, separately. His research interests include system reliability, reliability statistics, intelligent maintenance and lifetime theories, where he authored more than 100 papers.
Email: maxiaobing@buaa.edu.cn
Li Yang, Beihang University.
Li Yang is currently an Associate Professor with the School of Reliability and Systems Engineering, Beihang University. He received the Ph.D. degree in systems engineering from Beihang University, Beijing, China, in 2018. Prior to joining Beihang University, he was a Post-doctor with the Department of Mechanical and Industrial Engineering, University of Toronto. His research interests include intelligent maintenance, system reliability, prognostic, and risk analysis, where he has authored more than 70 papers.
Email: yanglirass@buaa.edu.cn
Han Wang, Beihang University.
Han Wang is currently an Associate Professor with the School of Reliability and Systems Engineering, Beihang University. He received the Ph.D. degree in systems engineering from Beihang University, Beijing, China, in 2020. His research interests include degradation analysis and reliability theory.
Email: wanghan@buaa.edu.cn
Download: Special Session #13.pdf
Suitable topics include but not limited to:
• Degradation process modeling and analysis
• Degradation-based reliability modeling and evaluation
• Bayesian reliability theories and applications
• Service lifetime evaluation and prediction
• Condition-based maintenance planning and decision-making
• Sensor-driven predictive maintenance optimization
• System availability analysis and optimization
• Deep/reinforcement learning-enhanced maintenance
Special Session #14
Safety Risk Monitoring and Early Warning
Session Organizers:
Xing Pan, Beihang University.
Professor Xing Pan is the head of the Department of Safety Science and Engineering, school professor at the School of Reliability and Systems Engineering at Beihang University. He is a certified quality management expert of AVIC Group. His main research interests focused in reliability and risk analysis, systems & SoS engineering theory and methods, as well as human-machine system safety analysis and human reliability analysis. From 2012 to 2013, he was a visiting scholar at the Department of Systems and Industrial Engineering, University of Arizona, Tucson, USA. He currently serves as an editorial board member of Systems Engineering and Electronic Technology, a young editorial board member of Journal of Safety and Environment.
Email: panxing@buaa.edu.cn
Jie Liu, Beihang University.
Doctor Jie Liu is current an associate professor in the School of Reliability and Systems Engineering at Beihang University. His research focus on trustworthy fault diagnosis, failure prognostics with data-driven methods in complex systems.
Email: liujie805@buaa.edu.cn
Download: Special Session #14.pdf
With the continuous development of new technologies, devices, and operational processes, industrial processes will face more risk threats. Safety risk monitoring and early warning technology is a commonly used approach for safety risk management. It analyzes the safety of current operating equipment or operational processes by monitoring various data. By combining the acceptable risk level, it provides early warning for impending unsafe events, ultimately achieving risk control. The ongoing development of intelligent operation and maintenance technology brings new opportunities and challenges for addressing safety risk monitoring and early warning in industrial processes. It encompasses artificial intelligence, big data, cloud computing, and other technologies, which support automated collection of risk data, risk data processing and analysis based on intelligent algorithms, automated early warning of anomalies or risk events, and intelligent response and control of unsafe events. This special conference seeks papers on the latest research, development, and applications of intelligent operation and maintenance technology in safety risk monitoring and early warning, with both theoretical and/or practical nature.
Suitable topics for this special conference include but are not limited to:
• Safety-oriented intelligent operation and maintenance architecture design
• Safety risk identification based on big data mining
• Safety risk assessment from the perspective of intelligent operation and maintenance
• Industrial risk early warning based on deep learning
• Emergency response and disposal of unsafe events
• Human factor safety risk assessment and monitoring
Special Session #15
Advanced technologies of reliability and state assessment for new energy storage systems
Session Organizers:
Yi Ren, Beihang University.
Ren Yi received the Ph.D. degree in reliability engineering and systems engineering from Beihang University. He is currently a professor, a reliability specialist and a member on the faculty of system engineering at the School of Reliability and System Engineering at Beihang University in Beijing, China. He has over 20 years of research and teaching experience in reliability engineering and system engineering. He is the Team Leader of the KW-GARMS reliability engineering software platform, and a holder of six Chinese ministry-level professional awards and one Chinese nation-level professional award. His recent research interests include reliability of complex systems, physics of failure，model based reliability system engineering (MBRSE), computer aided reliability engineering , and product life-cycle reliability engineering management. He has led over 20 projects supported by government, industries and companies, and has published over 100 papers, 2 books and 3 book chapters.
Email: renyi@buaa.edu.cn
Cheng Qian, Beihang University.
Dr. Cheng Qian is currently working as an Associate Professor in the School of Reliability and Systems Engineering, Beihang University. He considers himself as an experienced research scientist in reliability simulation, design and optimization of complicated products and systems. He has held more than 20 national funded and industrial projects, published over 80 research papers and 5 book chapters. He has also been active in academic activities as the associate editor and guest editor in 4 journals, special session chair of several international conferences, and committee member in the reliability branch of Chinese Institute of Electronics.
Email: cqian@buaa.edu.cn
Quan Xia, Beihang University.
Dr. Quan Xia is a postdoctoral fellow in the "Excellent Hundred Talents" talent program of Beihang University, Beijing, China, where he received the Ph.D. degree in Systems Engineering from Beihang University in 2020. He currently serves as young editorial board member of the Chinese scientific and technological core journal "Equipment Environmental Engineering". He also is a member of the China Aerospace Society and the China Instrumentation Society. He will join the School of Reliability and Systems Engineering, Beihang University as an associate professor this year. His research interest includes multi-physics simulations for reliability, system reliability assessment, AI for physical simulation and digital twins.
Email: quanxia@buaa.edu.cn
Download: Special Session #15.pdf
Rapid growth of the renewable energy (i.e., wind and solar) 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:
Special Session #16
Dynamic Mechanism, Fault Diagnosis, and Remaining Useful Life Prediction of Machinery
Session Organizers:
Prof. Lingli Cui
Lingli Cui, professor of mechanical engineering at the Beijing University of Technology, Beijing, China. Her current research focuses on dynamic modeling, fault mechanisms, pattern recognition, fault diagnosis, and remaining useful life prediction. She has presided over more than 10 completed and under research projects, and has published more than 160 peer-reviewed academic papers. She has been awarded the Second Prize of Ministry of Education Natural Science Award once and the Second Prize of Beijing Science and Technology Award once.
Email: cuilingli@bjut.edu.cn
Prof. Yongbin Liu
Yongbin Liu, Ph.D., professor in the School of Electrical Engineering and Automation, Anhui University. His main research interests include fault diagnosis and prognostics, remaining life prediction for rotating machine, and intelligent actuator and sensing. He has published over 60 journal and international conference papers, and some research are published in top journals in his field, such as RESS, JSV, TIM, etc. He also holds more than 20 invention patents in China. As a leading member, he has finished more than 10 projects, which are funded by the National Natural Science Foundation of China, the Key Research and Development Plan of Anhui Province, and some dominating enterprises in his field. He has won the second prize for Anhui Science and Technology award.
Download: Special Session #16.pdf
The development of scientific and reasonable maintenance strategies, based on real-world scenarios, is crucial to ensuring the safe and reliable operation of mechanical equipment. It is a major issue that needs to be addressed urgently. Prognostics and health management technology for key components play a vital role in ensuring the safety of the entire equipment, which include three key research directions: dynamic mechanisms, fault diagnosis, and remaining useful life prediction. This topic aims to explore accurate mechanical dynamics modeling methods, high-precision fault feature extraction and diagnosis methods, as well as highly reliable mechanism driven and data driven remaining useful life prediction methods.
The topics of interest include, but are not limited to：
Special Session #17
Condition monitoring, fault diagnosis and intelligent maintenance of wind energy equipment
Session Organizers:
Ling Xiang, Ph.D., professor in the School of Mechanical Engineering, North China Electric Power University. 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. She has over 80 published journal and international conference papers. She also published his results in top journal in the field, such as MSSP, ECM, AE, etc. As a leading member, she has finished more than 20 projects, which are funded by National Natural Science Foundation of China, National "863" Program, and some dominating enterprises in her field. She also holds 10 invention patents in China.
E-mail: xiangling@ncepu.edu.cn
Prof. Baoping Tang is currently a professor and Vice dean of College of Mechanical and Vehicle Engineering. He is an expert enjoying the special allowance of the State Council Government and the Principal Investigator of National Key R&D Projects, has been selected as the national candidates for the "Hundred Thousand Ten Thousand Talents Project". He also serves as the deputy director of the Dynamic Testing Committee of the Chinese Society of Vibration Engineering. His research interests focus on intelligent operation and maintenance of mechanical and electrical equipment, measurement technology, virtual instrument and wireless sensor network. Prof. Tang is the PI over more than 30 research projects including national key R&D projects, National Natural Science Foundation of China, National "863" Program, and has published more than 180 papers. Prof. Tang has received the second prize of National Technology Invention Award at 2004, the second prize of National Science and Technology Progress Award at 2015, the second prize of National Teaching Achievement Awards at 2018.
E-mail: bptang@cqu.edu.cn
Zhongsheng Chen, Ph.D., Professor in the College of Automotive Engineering, Changzhou Institute of Technology. His main research interests include advanced vibration signal processing, fault diagnostics and prognostics, vibration energy harvesting, deep learning-based machine vision. He is now focusing on intelligent maintenance of key equipments, simultaneous vibration suppression and energy harvesting, machine-based defect detection, etc. As a leading member, he has participated in five projects, which are funded by the National Natural Science Foundation of China. He has over 100 published journal and international conference papers, such as IEEE TPE, ASME, MSSP, etc. He also holds 22 invention patents in China.
E-mail: chenzs@czu.cn
Download: Special Session #17.pdf
Renewable energy sources are receiving increasing attention. In recent years, as the main force of renewable energy, wind power has become increasingly important in the field of power generation. With the steady growth of wind power generation scale, the failure of wind turbines has gradually become a challenge for their development. Nowadays, not only university researchers in related fields but also large wind power system enterprises are also committed to the research and development of condition monitoring and fault diagnosis technology of wind turbines. Researching effective methods for monitoring and diagnosing wind turbine conditions, comprehensively improving the operational safety and stability of wind turbines, improving the quality and efficiency of wind power generation, is an important trend in line with the current development of renewable energy 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 wind power systems, 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:
Download: Special Session #21.pdf
Special Session #18
Special Session on Spare parts & Logistics
Session Organizers:
Linhan Guo, Beihang University
He is an associate professor of the School of Reliability and Systems Engineering at Beihang University. He received his PhD in Industrial System Engineering at Beihang University. He was a visiting scholar in the department of Industrial and System Engineering at Rutgers University. His research interests include logistics engineering, system availability modeling and simulation, spare parts supply chain model and optimization.
E-mail: linhanguo@buaa.edu.cn
Yi Yang, Beihang University
She held a Ph.D. from Nanjing University of Science and Technology, in 2008. She was engaged in postdoctoral research at the School of Reliability and Systems Engineering, Beihang University (BUAA). Since 2019, she has been a professor at the School of Reliability and Systems Engineering, Beihang University.
Her main research interests include Reliability analysis and design, Traffic network, Control science and engineering, CPS, Multimodal learning and knowledge graph. She serves as a deputy editor of CAAI Artificial Intelligence Research.
E-mail: yiyang@buaa.edu.cn
Meilin Wen, Beihang University
She is an Associate Professor affiliated with the School of Reliability and Systems Engineering at Beihang University, Beijing, China. She received her PhD in Mathematics of Tsinghua University, China. She was a visiting scholar in the University of Hong Kong, the Chinese University of Hong Kong, and UBC University in Canada successively. Her research is focused on belief reliability analysis, uncertain theory and uncertain optimization.
E-mail: wenmeilin@buaa.edu.cn
Download: Special Session #18.pdf
Spare parts & logistics are essential for Intelligent operation and maintenance of equipment, which enhances the effectiveness and reduce the costs of the supported equipment. This special session focuses on optimizing the footprints of the spare parts and logistics by employing modeling, simulation and intelligent technologies. Academics working on the following topics (not limited to) are sincerely welcomed.
• Decision on spare parts
• Supply chain of maintenance & operation
• Logistics modeling
• Multimodal learning on spare parts & logistics
• Knowledge graph on spare parts & logistics
• Big data on spare parts & logistics
• Location & allocation
• Inventory control & optimization
• Availability modeling with spare parts & logistics
Special Session #19
Special Session on Digital twin and modeling of rotary machines
Session Organizers:
Huaitao Shi, Shenyang Jianzhu University: He got his PhD degree at Northeast University (NEU) in 2012. He has been the vice dean of school of mechanical engineering since 2015, and was appointed as a full professor in 2019. His main research interests cover fault diagnosis and digital-twin techniques, dynamic modelling of rotary machines and intelligent industrial robots. He has hosted two special issues in scientific journals, and was appointed as the associate editor of Journal of Field Robotics(JFR). He has presided over 20 projects, and has published more than 90 academic papers, of which 2 ESI highly-cited papers were highly praised by peers. He has won 8 provincial science and technology awards including 3 first prizes, and was awarded national and provincial outstanding young talents.
Yupeng Li, Shenyang Jianzhu University: She received a doctorate in Structural engineering and a master's degree in applied mechanics from the School of Engineering and Applied Science, Washington University in St. Louis in St. Louis in 2005. She was appointed as the Vice President of Shenyang Institute of Architecture in 2018. Her research directions mainly include reliability of hybrid integrated circuits, performance analysis of composite material structures, industrial intelligence, and digital twin. She has led over 10 large-scale projects at Intel Corporation and has published more than 20 papers in internationally renowned publications such as AIAA Journal, International Journal of Frature, Composite Structure, IEEE, and others.
E-mail: lyp@sjzu.edu.cn
Xiaotian Bai, Shenyang Jianzhu University: He got his bachelor degree from Dalian University of Technology(DUT) in 2011, and finished the doctoral study in 2016. He was appointed as the associate professor in Shenyang Jianzhu University in 2019. His research interests mainly include vibration and sound radiation control of rotary machines, fault diagnosis, digital-twin, signal processing and meta-materials. He has published 41 academic papers in the past five years, and was granted over 10 patents. He has also published one monograph, and the research findings got him 3 provincial awards. He has presided 2 national projects, 5 provincial projects, and served as reviewer for numbers of internationally renowned journals.
E-mail: lyp@sjzu.edu.cn
Download: Special Session #19.pdf
The digital twin and modeling of rotating machines are crucial for its health maintenance and management, which improves the reliability of rotating machines and reduces maintenance costs. This special session focuses on investigating condition monitoring and fault diagnosis of rotating machines by employing digital-twin, simulation, modeling, and intelligent technologies. Academics working on the following topics (not limited to) are sincerely welcomed.
Special Session #20
Diagnosis and operation and maintenance of rail transit
Session Organizers:
Professor Zhongkui Zhu, Soochow University
Dr. Zhiqiang Zhang, CRRC Qingdao Sifang Co., Ltd.
Professor Jianwei Yang, Beijing University of Civil Engineering and Architecture
Download: Special Session #20.pdf
Economic and social development in most countries has increased considerably the requirement for transportation capability. Railway transportation has played an important role in this development due to its strong transportation capability and high speeds. The continuous operation of trains is crucial in ensuring fluid and efficient traffic circulation. However, failure of train components can result in unexpected breakdowns, which can lead to serious traffic accidents. Diagnosis and operation and maintenance of rail transit have recently come to play a crucial role. Their signals including vibration signals, acoustic signals, images, etc., are sensitive to abnormal/fault conditions, which usually shows the characteristics of impulsive transients. However, these repetitive transients are typically weak, especially when the equipment starts its fault at the initial stage. Moreover, environmental noises cause further interference for extracting fault information. To overcome the aforementioned difficulties and promote intelligent condition monitoring, a focused session in this area will be organized as a platform to present high-quality original research on the latest developments of condition monitoring methods for rail transit. Potential topics include but are not limited to the following:
Special Session #21
Deep Transfer Learning and Meta Learning Towards Fault Diagnosis and Prognosis of Machine Systems
Session Organizers:
Prof. Weihua Li
Prof. Li received his Ph.D. degree in mechanical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2003. He is currently the Dean and a full Professor with the School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China. His research interests include Industrial intelligence, industrial big data, digital twins, intelligent maintenance & health management, and intelligent connected vehicles.
He is now serving as the co-chair of Technical Committee (TC-3) on Condition Monitoring & Fault Diagnosis Instrument, IEEE IM Society. He serves Associate Editors of IEEE Transactions on Instrumentation and Measurement, IEEE Sensor Journal, and Editorial Board Member of several other journals. He is the PI (principal investigator) of over 20 projects which are funded by National Natural Science Foundation of China, National Key Research and Development Program of China, Key Research and Development Program of Guangdong Province, University-Industry Cooperation, etc. Prof. Li has published over 150 papers and 4 books.
Email: whlee@scut.edu.cn
Prof. Fengtao Wang
Prof. Wang received his PhD degree in mechanical engineering from Dalian University of Technology, Dalian, China, in 2003. He is currently a full Professor of Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education, Department of Mechanical Engineering, College of Engineering, Shantou University. His research interests include additive manufacturing, process monitoring and control, intelligent fault diagnosis and remaining useful life prediction. He has led over 20 projects and published more than 100 academic papers. He won the Second Prize of the 2017 National Science and Technology Progress Award.
Email: ftwang@stu.edu.cn
Research Associate Zhuyun Chen
Dr. Chen received his Ph.D. degree in mechanical engineering from South China University of Technology, Guangzhou, China, in 2020. Funded by Elite Youth Program of Guangzhou Government, he was also an international scholar in KU Leuven, Belgium from 2017 to 2018. He is currently a research associate and postdoctoral research with School of Mechanical and Automotive Engineering, South China University of Technology. His research interests include mechanical signal processing, and intelligent diagnosis and prognosis for complex systems.
Email: mezychen@scut.edu.cn
Download: Special Session #21.pdf
The topics of interest include, but are not limited to:
• Knowledge transfer for complex systems
• Intelligent monitoring of additive manufacturing process
• Transfer learning techniques for fault diagnosis and prognosis
• Meta learning algorithms for fault diagnosis and prognosis
• Explainable neural network models for industrial equipment
• Novel feature selection and extraction approaches for intelligent maintenance