Time: Wednesday, April 12th, 15:30-17:00 PM
Location: Conference Room 309, Main Building
Reported by: Piao Chen (Chen Piao)
Reported by:
Dr. Piao Chen is a tenured assistant professor in statistics at Delft Institute of Applied Mathematics, Delft University of Technology. He obtained his PhD in Industrial and Systems Engineering Management from National University of Singapore in 2017, and Bachelor in Industrial Engineering from Shanghai Jiao Tong University in 2013. Dr. Chen’s research focuses on industry big data analytics, reliability engineering, and statistical learning. Most of his work has appeared in top journals in statistics and engineering, including Technometrics, Statistica Sinica, Journal of Quality Technology, IEEE Transactions on Information Theory and IEEE Transactions on Reliability. His work received the Best Paper Award of SRSE2022 and was listed as the annual key achievement by A*STAR.
Introduction to report content:In this talk, we propose a framework to analyze accelerated degradation testing (ADT) data in the presence of inspection effects. Motivated by a real dataset from the electric industry, we study two types of effects induced by inspections. After each inspection, the system degradation level instantaneously reduces by a random value. Meanwhile, the degrading rate is elevated afterwards. Considering the absence of observations due to practical reasons, we employ the expectation –maximization (EM) algorithm to analytically estimate the unknown parameters in a stepwise Wiener degradation process with covariates.
(Organized by: Department of Management science and Logistics, Scientific Research and Academic
Center)
