Data-Driven Reliability Modeling for Failure Data Analysis and Risk Prediction
Modern reliability engineering is increasingly focused on addressing the challenges posed by stochastic and highly complex systems. In response to these complexities, data-driven approaches have emerged as powerful tools for decision-makers to identify failure patterns, optimize maintenance strategies, and effectively mitigate potential risks, leading to improved system reliability. This session discusses the latest developments in data-driven methodology for failure data analysis and risk prediction, highlighting the significance of employing data-driven models to improve maintenance strategies and decision-making processes.
Chair: Jinyang Wang, National University of Singapore, Singapore
Jinyang Wang is currently pursuing the Ph.D. degree with the Department of Industrial Systems Engineering and Management, National University of Singapore. His research interest includes reliability engineering, semi-parametric statistical inference and asymptotic efficiency. His work has appeared in Scandinavian Journal of Statistics. (E-Mail: firstname.lastname@example.org)
王瑾旸，新加坡国立大学工业工程系在读博士。主要研究方向为可靠性工程，半参数统计推断和渐近有效性分析。其研究成果发表在统计领域期刊Scandinavian Journal of Statistics中。
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