Special Session IX

Data-driven Methodology on Maintenance, Warranty, and Failure Data Analysis
数据驱动方法在维护、质保、以及失效数据分析的应用

This session discusses recent advances of data-driven methodologies used for repairable or non-repairable system modeling. The session uses applications on lifetime data analysis, maintenance optimization, warranty pricing, and remaining useful life estimation to illustrate these state-of-the-art models.
本分会将讨论关于数据驱动模型在可修或不可修系统上的最新进展。分会将主要展示该类方法在生存数据分析、维护优化、质保定价以及剩余寿命估计的应用。

Related topics:

Survival analysis, warranty, degradation, maintenance

 


Chair:

Qiuzhuang Sun received the joint B.E. degree in industrial engineering and in computer science from Shanghai Jiao Tong University in 2015, and the Ph.D. degree in industrial and systems engineering from the National University of Singapore in 2019. He is currently a Research Fellow with the Department of Industrial Systems Engineering and Management, National University of Singapore. He will join the School of Mathematics and Statistics, University of Sydney as a Lecturer (Assistant Professor) this year. His research interests include maintenance modeling, degradation analysis, and data-driven optimization. (Mail: isesq@nus.edu.sg)

Co-chair:

Shixiong Wang received the B.Eng. degree in detection, guidance, and control technology and the M.Eng. degree in systems and control engineering from Northwestern Polytechnical University in 2016 and 2018, respectively. He is expected to obtain the Ph.D. degree in Industrial Systems Engineering and Management from National University of Singapore in 2022. His research interests include statistics and optimization theories with applications in signal processing (especially optimal estimation theory) and control technology.


Submission Portal

If you are interested in the special session, please submit your paper or abstract here
http://confsys.iconf.org/submission/srse2021-session9

For any questions, please mail the organizer or conference secretary srse@sciei.org.

 

© SRSE 2019-2021 | Harbin, China | Email: srse@sciei.org