应数学与统计学院、甘肃省数学与统计学基础学科研究中心邀请，浙江工商大学徐安察教授将为我院师生作学术报告。
报告题目：A Robust Bayesian Framework for Degradation State Identification in the Presence of Outliers
报告摘要：Accurate degradation state estimation is critical for predictive maintenance, yet it is often compromised by measurement outliers and parameter uncertainty. Existing methods either assume Gaussian measurement errors, which are sensitive to outliers, or overlook parameter uncertainty, leading to overconfident predictions. To address these challenges, we propose a Bayesian online degradation state estimation framework that integrates robust error modeling with parameter uncertainty quantification. Specifically, we model measurement errors using a Student’s t distribution to handle outliers and employ variational Bayes with Laplace and Gamma approximations to efficiently estimate posterior distributions of degradation states and parameters. This framework enables real-time updates, ensuring adaptability to dynamic operating conditions. Based on the estimated degradation states, we further derive real-time remaining useful life predictions and dynamic maintenance strategies under a cost function model. Numerical experiments and case studies demonstrate the framework’s robustness, computational efficiency, and practical applicability.
报告时间：2025年9月18日 16:30
报告地点：致勤楼D07学术报告厅
邀请人： 颜荣芳教授 张建东副教授
届时欢迎广大师生参与交流！
【报告人简介】
徐安察，浙江工商大学统计学教授，博士生导师，主要致力于可靠性数据分析与建模、贝叶斯在线学习、深度学习理论与应用的研究。迄今以第一作者或通讯作者在Naval Research Logistics、Journal of Quality Technology、European Journal of Operational Research、IEEE Transactions on Reliability、IISE Transactions等国际可靠性及统计主流杂志上发表SCI论文50余篇，其中ESI高被引论文5篇。主持国家及省部级项目10余项，获浙江省自然科学奖、福建省自然科学奖、第一届全国统计科学技术进步奖等。目前担任中国运筹学会可靠性分会副理事长、Statistical Theory and Related Fields副主编。
数学与统计学院
甘肃省数学与统计学基础学科研究中心
2025年9月16日
