Invited Speeches

Speech Title: Reliability Analysis of a Hierarchical System with Dependent Failure Time Data
Abstract: Reliability evaluation of a hierarchical system is imperative yet challenging because the reliability test of a full-scale system is usually expensive and time-consuming. As a practical case, the collected failure time data is dependent since it is collected from the same system within the same time period, leading to the so-called “overlapped” failure data. Misuse of such information in reliability analysis will lead to conflicting or even erroneous results. In this talk, we discuss how to effectively and efficiently use such data for system reliability evaluation. The proposed approach has the potential to optimize the system configuration using less full-scale test data in terms of reliability improvement with lower experiment cost.

Bio: Lechang Yang is a Professor of Reliability Engineering at the University of Science and Technology Beijing, China. Given a multidisciplinary background, his research fields include mechanical engineering, system engineering, and industrial engineering, but all are centered around reliability and risk problems. He is the PI of many research projects such as the National Natural Science Foundation of China granted project, the Basic and Applied Basic Research Foundation of China granted projects, the Aeronautical Science Foundation of China granted project and China Postdoctoral Science Foundation granted project. He has published more than 60 peer-reviewed papers and earned over 1200 citations with an H-index 17. Such research results are appreciated by academic communities (Best Paper Award in international academic conferences) and are recognized by engineering practitioners (First Prize of China Association of Plant Engineering Technology Award).

Prof. Qing Ni
Northwestern Polytechnical University, China

Speech Title: Prior Knowledge Empowered Large Models for Intelligent Maintenance
Abstract: The reliability of high-end equipment is critical to modern industry. Existing condition monitoring paradigms, including physics-based model, signal-processing-based, and conventional machine-learning approaches, each suffer from inherent limitations. With the emergence of large models, intelligent condition monitoring is witnessing unprecedented opportunities. However, general-purpose large models, trained without industrial domain knowledge, remain limited by weak interpretability, poor cross-condition generalisation, and hallucinations, preventing them from meeting the stringent reliability requirements of industrial diagnosis. Prior knowledge provides the key to bridging this gap. Building on this foundation, the intelligent diagnosis multimodal large model and the intelligent graph-based decision-making large model are presented, showing the potential of prior-knowledge-empowered large models as reliable, interpretable, and trustworth solutions for intelligent maintenance.

Biodata: Qing Ni is a Professor at Northwestern Polytechnical University. Her main research interests include machine condition monitoring based on large language models, physics-informed neural networks, digital twins, and signal processing. She was awarded the National Young Talent Program in 2024 and listed among the World’s Top 2% Scientists in 2024 and 2025 by Stanford University. She has published over 60 SCI-indexed papers with more than 4800 citations, and over 10 papers have been designated as ESI Hot Papers or Highly Cited Papers. She is currently serving as an associate editor or editorial board member for several prestigious journals, such as IEEE Transactions on Industrial Informatics, Engineering Applications of Artificial Intelligence, Neurocomputing, Journal of Intelligent Manufacturing, IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, etc.

Prof. Guanghan Bai
National University of Defense Technology, China

Speech Title: Reliability and Resilience of Unmanned Intelligent Swarm
Abstract: Inspired by biological collective intelligence, unmanned swarm systems composed of numerous autonomous platforms can accomplish missions as an integrated whole by mimicking the collaborative behaviors and information exchange of gregarious organisms. Owing to the distributed and self-organizing nature, these swarms can generate complex collective behaviors from simple local interactions, giving rise to novel combat styles such as swarm tactics. Thus, traditional platform-centric, static reliability assessment methods are not appropriate for large-scale swarms operating under complex and dynamic environments. Therefore, we discuss an intelligent swarm reliability evaluation framework, covering mission reliability modeling, importance analysis, and structure optimization for UAV swarms, as well as reliability modeling of information interaction systems based on multi-state networks. In addition, when individual agents fail or crash due to malfunctions, environmental factors, or enemy interference, the swarm is capable of absorbing and adapting to disruptions through self-organization and self-reconfiguration, which is regarded as resilience. A mission-oriented intelligent swarm resilience evaluation framework will be discussed, including the network-based resilience evaluation by considering communication limits, dynamic resilience evaluation for cross-domain swarms in confrontation, resilience evaluation for UAV swarms in joint reconnaissance mission, and resilience evaluation of unmanned autonomous swarm with informed agents under partial failure, etc. We also cover some topics, such as preventing swarm-splitting, suppressing cascading failure by considering logistic support, preventing stealth attackers based on a vigilance mechanism, improving friendly swarm information exchange capacity, and disintegrating enemy’s information exchange networks.

Bio: Guanghan Bai, Ph.D., Associate researcher at the Key Laboratory of Equipment State Sensing and Smart Support, College of Intelligence Science and Technology, National University of Defense Technology. He received his Bachelor’s degree in Management Engineering in 2009 and Master’s degree in Management Science and Engineering in 2011 from the National University of Defense Technology. He obtained his Doctor’s degree in Engineering Management from the University of Alberta, Canada in 2016. His research focuses on reliability and resilience of complex systems, network reliability and resilience analysis. He has led over 10 research projects, including 2 National Natural Science Foundation of China, 2 Hunan Provincial Natural Science Foundation (Distinguished Young Scientists and Excellent Young Scientists), and etc. He has published over 50 SCI-indexed papers in top-tier international journals, such as RESS, IEEE IoT, CIE, IEEE TR, DT, CSF, IISE, Chaos, etc. He is the recipient of the Most Influential Paper Award (2014–2023) from the Department of Engineering Management of the Chinese Academy of Engineering, the IEEE Reliability Society Outstanding Young Scholar Award, the IEEE Reliability Society 2024 Best Conference Paper Award, and Best Paper Awards at international conferences such as APARM 2018 & QR2MSE 2018, PHM 2022/2024, ICRMS 2025, and ISSSR 2025. Dr. Bai has co-authored 2 academic monograph, and holds 5 authorized invention patents and 2 software copyrights. He has also led the development of 3 group standards in the “Complex System Reliability and Safety” series for the Chinese Institute of Command and Control. He is also the recipient of “Outstanding Young Talents” of NUDT’s High-Level Innovative Talent Program, the World’s Top 2% Scientists in 2025 and First Prize of the Science and Technology Progress Award by the China Shipbuilding Engineering Society.

Prof. Naipeng Li
Xi'an Jiaotong University, China

Speech Title: Data-model Linkage Remaining Useful Life Prediction for Mechanical Transmission Components
Abstract: With the advancement of information and sensing technologies, predictive maintenance has become a vital guarantee for the safe service of equipment. Accurate prediction of the remaining useful life (RUL) of mechanical equipment serves as the fundamental prerequisite for implementing predictive maintenance. Targeting critical challenges in the remaining useful life prediction of mechanical transmission components such as wind turbine drive trains, including ambiguous degradation mechanisms and dynamically time-varying degradation processes, the speaker has carried out systematic research. This work proposes a stochastic degradation modeling theory considering multiple random influencing factors, establishes a novel digital-model collaborative matching method for life prediction, and develops multiple health assessment and prediction systems. These technologies have been applied to wind turbines, industrial robots and other equipment, achieving favorable engineering application performance.

Bio: Naipeng Li is a Professor and Doctoral Supervisor at Xi’an Jiaotong University. His research focuses on complex electromechanical equipment including wind turbines, industrial robots and space exploration devices, covering autonomous condition perception, remaining useful life prediction, intelligent control, and large-scale operation & maintenance models. He has presided over more than 10 research projects, including the Excellent Young Scientists Fund (Category B), General Program and Young Scientists Fund of the National Natural Science Foundation of China (NSFC), as well as industrial collaborative projects. He serves as a Youth Editorial Board Member for multiple journals such as Measurement and Science China Technological Sciences. He has authored one English monograph and published over 40 high-quality academic papers, which have garnered more than 5,000 total citations in the Web of Science core collection. He participated in formulating 4 national standards, holds over 30 authorized national invention patents, and has transferred 6 patented technologies to industrial partners. His research outcomes have been applied to wind power equipment, industrial robots, heavy-duty trucks and other machinery. His major honors and awards include: First-Class Prize of Shaanxi Provincial Natural Science Award (2nd completer), two First-Class Prizes of Shaanxi University Science and Technology Award (1st and 2nd completer respectively), First-Class Prize of Natural Science Award from the Chinese Association of Automation (4th completer), First-Class Prize of Ministry of Education Technological Invention Award (5th completer), and Outstanding Doctoral Dissertation of Shaanxi Province. He has been selected for the Young Talent Support Project of the China Association for Science and Technology, Clarivate Highly Cited Researchers, and Elsevier World’s Top 2% Scientists.

Prof. Piao Chen
Zhejiang University, China

Speech Title: Maintenance Operations for Redundant Systems with Imperfect Condition Information
Abstract: Redundant systems are widely used when service continuity is critical, but their maintenance remains difficult when managers observe component conditions only through periodic and imperfect inspections. In such settings, each inspection provides useful but noisy condition information, and the manager must decide not only when to inspect but also how many components to replace in a system whose redundancy creates system-level risk interactions. We study this maintenance-operations problem for K-out-of-N systems, in which the system functions whenever at least K of N components are operational. Component degradation is modeled as a three-state continuous-time Markov chain, and inspection outcomes are represented by a state-observation matrix that captures imperfect condition information. We formulate the problem as a partially observable Markov decision process in which posterior defect probabilities summarize the operational condition of functional components. For a 1-out-of-2 system, we establish a two-dimensional control-limit structure for the optimal replacement policy and develop a value-iteration algorithm to compute the joint inspection and replacement policy. We then extend the framework to general K-out-of-N systems and develop a structure-guided point-based value-iteration algorithm to address the high-dimensional belief space. Numerical studies based on a radar transmitter system and a 4-out-of-6 configuration show how inspection cost, replacement cost, downtime cost, and inspection accuracy jointly shape inspection cadence and replacement scope. The results highlight when managers should wait, selectively replace the riskiest components, or bundle replacements, and show that system redundancy can change whether a cost parameter primarily affects inspection timing or replacement thresholds.

Bio: Piao Chen is an Associate Professor at Zhejiang University and previously was an Assistant Professor at TU Delft. His main research interests include quality and reliability, statistical learning, and data-driven optimization. He has published more than 40 papers in leading interdisciplinary journals in statistics, engineering, and management, including Management Science, Production and Operations Management, INFORMS Journal on Computing, and IEEE Transactions on Information Theory. His work has received multiple Best Paper Awards at international conferences.

Prof. Qingqing Zhai
Shanghai University, China

Speech Title: Multivariate Degradation Modeling considering Unit-to-Unit Heterogeneity
Abstract: Modern engineering systems are growing increasingly complex, not merely in terms of structural hierarchy and functional integration, but also because their overall health status cannot be adequately represented by any single component or performance characteristic (PC). Moreover, owing to physical coupling among components and the influence of a shared operating environment, the degradation processes of different components or PCs are generally interdependent. Accurate modeling of such multivariate degradation is essential for identifying failure mechanisms and providing critical information for prognostics and health management. In this talk, we systematically review existing multivariate degradation modeling methods, including general path models, stochastic process models, copula-based approaches, and other hybrid frameworks. In addition, we introduce a novel multivariate degradation model for heterogeneous products, which draws on the concept of acceleration from survival analysis.

Bio: Qingqing Zhai is an Associate Professor in the School of Management, Shanghai University. He received his Ph.D. degree in Systems Engineering from Beihang University in 2015, and was a research fellow in the Department of Industrial Systems Engineering and Management, National University of Singapore from 2015 to 2017. His main research interests include statistical degradation modeling, probabilistic reliability modeling and game theories in reliability. He has published over 50 papers in leading journals, such as Journal of the American Statistical Association, Technometrics, IISE Transactions, IEEE Transactions on Reliability, Reliability Engineering & System Safety, IEEE Transactions on Industrial Informatics, and European Journal of Operational Research. He was awarded the Best Paper Award from the Industrial Electronics Society of IEEE in 2018.

Dr. He Li
Liverpool John Moores University, UK

Speech Title: Reliability and Maintenance of Floating Offshore Wind Systems
Abstract: This talk will highlight key aspects of the reliability and maintenance of floating offshore wind systems, including turbines and wind farms. It will begin with an overview of the development of a failure dataset, AI-based data analysis methods, and new insights into failure modes and maintenance characteristics of floating offshore wind turbines. The presentation will cover failure analysis and prevention strategies, along with the design of a health monitoring system tailored for floating wind turbines. Transformation-based reliability analysis methodologies will also be introduced to predict reliability characteristics, especially in the context of limited domain knowledge about these emerging systems. Furthermore, real-time, risk-based opportunistic maintenance strategies for floating wind turbines and farms will be presented. The session will conclude with an availability estimation, demonstrating how maintenance scheduling performance can be evaluated. Together, these insights aim to improve the cost-effectiveness and safety of operations in floating offshore wind farms.

Bio: He Li is a researcher at Liverpool John Moores University in the UK. He received a PhD from the University of Electronic Science and Technology of China, China and the University of Lisbon, Portugal respectively. His research primarily focuses on the failure, risk, reliability, and maintainability of complex systems. Dr. Li is a Marie-Curie Fellow and a top 2% scientist. He has coordinated several projects in terms of the operation and maintenance of offshore systems funded by Europe and China and published a number of monographs, peer-reviewed journal papers, book chapters, and conference papers, including several highly cited/hot papers as well as best paper awards. Dr. He Li is a Fellow of the International Society of Engineering Asset (ISEAM) and a Technical Committee Member of the European Safety and Reliability Association (ESRA). He also won several awards, like A ‘Design (The only Gold Award in 2023), and was recognized as an Emerging Leader by the MST Journal and a Rising Star of LJMU, UK (the only awardee of 2025). He has been an associate editor of the Complex & Intelligent Systems Journal and the Journal of Mechanical Science and Technology, etc. Dr. He Li has also been a Technical Program Chair, an Organization Co-Chair, a Publication Co-Chair, a session chair, and a program committee member of more than 20 international conferences.