Data-Driven Reliability Modeling for Failure Data Analysis and Risk Prediction
故障数据分析和风险预测中的数据驱动可靠性建模
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.
包含随机性的高度复杂系统在当今可靠性工程领域日益备受关注。为了研究这类复杂系统，决策者们使用数据驱动分析方法作为识别故障模式、优化维护策略和降低潜在风险的强大工具，从而提高系统的可靠性。本次会议主要讨论运用数据驱动方法进行故障数据分析和风险预测的最新进展，尤其是数据驱动可靠性建模在改进维护策略和决策过程中的重要性。
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: jinyangwang@u.nus.edu)
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。
Submission Portal
If you are interested in the special session, please submit
your paper or abstract here
https://www.zmeeting.org/submission/srse2023 and choose special session 11.
For any questions, please mail the organizers or conference secretary srse@sciei.org.
© SRSE 2019-2024 | Hangzhou, Zhejiang, China | Email: srse@sciei.org