题目：A Robust Bayesian Framework for Degradation State Identification in the Presence of Outliers

报告人：徐安察 教授（浙江工商大学）    

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.    

报告人简介：徐安察，浙江工商大学统计学教授，博士生导师。研究方向包括: 退化数据分析与建模、贝叶斯在线学习、寿命数据分析。迄今以第一作者或通讯作者在NRL、JQT、IISE Transactions、EJOR等国际可靠性及统计主流杂志上发表SCI论文50余篇。主持国家级与省部级项目十余项，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、国内统计英文期刊《Statistical Theory and Related Fields》副主编。    

报告时间：2025年5月10日（周六）下午15：30-16：30    

报告地点：教二楼513    

联系人：胡涛