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报告题目: Remaining useful life prediction for two-phase degradation model based on reparameterized inverse Gaussian process

报告人: 徐安察 教授（浙江工商大学）

Abstract

Two-phase degradation is a prevalent degradation mechanism observed in modern systems, typically characterized by a change in the degradation rate or trend of a system's performance at a specific time point. Ignoring this change in degradation models can lead to considerable biases in predicting the remaining useful life (RUL) of the system, and potentially leading to inappropriate condition-based maintenance decisions. To address this issue, we propose a novel two-phase degradation model based on a reparameterized inverse Gaussian (rIG) process. The model considers variations in both change points and model parameters among different systems to account for subject-to-subject heterogeneity. The unknown parameters are estimated using both maximum likelihood and Bayesian approaches. Additionally, we propose an adaptive replacement policy based on the distribution of RUL. By sequentially obtaining new degradation data, we dynamically update the estimation of model parameters and the RUL distribution, allowing for adaptive replacement policies. A simulation study is conducted to assess the performance of our methodologies. Finally, a Lithium-ion battery example is provided to validate the proposed model and adaptive replacement policy.

报告人简介: 徐安察，浙江工商大学统计与数学学院，教授，博士生导师，“浙江省高校领军人才培养计划”高层次拔尖人才，浙江省高校中青年学科带头人。担任第十一届中国运筹学会可靠性分会副理事长，中国现场统计研究会可靠性工程分会常务理事。主要研究领域为退化数据分析与建模、贝叶斯在线学习、客观贝叶斯方法、寿命数据分析等。先后主持国家自然科学基金3项，省部级项目4项，在《IEEE Transactions on Reliability》、 《Reliability Engineering & System Safety》、《Computational Statistics & Data Analysis》、《Journal of Statistical Planning and Inference》、《Computers & Industrial Engineering》等期刊上发表论文40多篇，获第一届全国统计科技进步奖三等奖。

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