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报告题目: Statistical Inference for the Inverse Gaussian Process

报告人: 陈飘 副教授（浙江大学）

Abstract

Degradation modelling plays a pivotal role in understanding the lifecycle of various systems. The Inverse Gaussian (IG) process stands out as a crucial model, particularly for monotone degradation data. This talk delves into enhancing statistical inference for the IG process, addressing both offline and online aspects. In the first half, we explore offline estimation of model parameters and reliability characteristics. In particular, the existing interval estimation methods are either inaccurate given a moderate sample size of the degradation data or require a significant computation time when the size of the degradation data is large. To bridge this gap, we develop a general framework of interval estimation for the IG processes based on the method of generalized pivotal quantities. In the second half, we shift focus to online estimation and the prediction of remaining useful life (RUL). We present innovative online estimation algorithms tailored for swift and accurate parameter updates whenever new degradation measurements become available. Furthermore, we derive the distribution of RUL, allowing for recursive updates as more data accumulates. Several real degradation datasets are used for illustration.

报告人简介: Dr. Piao Chen is an associate professor at ZJUI Institute, Zhejiang University. Prior to this position, he served as an assistant professor in statistics at TU Delft during 2020-2023. He earned his Ph.D. in Industrial and Systems Engineering Management from the National University of Singapore in 2017 and holds a Bachelor's degree in Industrial Engineering from Shanghai Jiao Tong University, obtained in 2013. His research primarily focuses on industry big data analytics, reliability engineering, and statistical learning. His work has been published in top-tier journals in the fields of statistics and engineering, including Technometrics, the Journal of Quality Technology, IEEE Transactions on Information Theory, and IEEE Transactions on Reliability. His work received Best Paper Award at the International Conference on System Reliability and Safety Engineering (SRSE2022), the INFORMS Conference on Quality, Statistics, and Reliability (ICQSR2023), and the International Workshop on Statistical Theory and Related Fields (STARF2023).

报告时间：2023年10月23日（周一）上午10：00-12：00

报告地点：教二楼727

联系人：胡涛