Aims & Scope

Digital twin has been considered as an emerging approach that benefits the development of many research areas and disciplines. Driven by requirements from Physics-of-Failure and machine-learning-based reliability design and analysis methods, highly accurate prediction of failures under dynamic uncertain conditions has become a key concern in the synergistic design of products considering both reliability and functional performance. This has given rise to a novel multidisciplinary research area, Reliability Digital Twin (RDT), which fully utilizes multidimensional data collected from products, including product model data, statistical data of fault events, real-time operational status data, and historical environmental and load data, to provide more accurate simulations and reliability predictions through digital twin. Related innovative ideas and solutions have emerged worldwide in recent years. This session is organized to present these advances from both theoretical and application perspectives to academic and engineering communities.

Submissions that reflect the session scope and current state of the field are welcome in areas including but not limited to:

• • Methodologies and applications combining reliability and digital twin
• • Development of RDT for products during design and maintenance stages
• • AI and LLM for RDT
• • Uncertainty analysis in RDT
• • Advanced application research of RDT
• • Intelligent predictive maintenance using RDT
• • PHM on embodied intelligence devices using RDT
• • PHM on swarm intelligent systems using RDT

Call for Presentations

Call for Presentations is now open. Researchers and industry professionals are invited to submit presentation proposals.

Please see the Call for Presentations for details.