报告主题：Efficient Online Estimation and Remaining Useful Life Prediction Based on the Inverse Gaussian Process

时间：2024年5月31日14:00-15:00

地点：上川路校区一教218室

报告人：徐安察

报告内容简介：

Fast and reliable remaining useful life (RUL) prediction plays a critical role in prognostic and health management of industrial assets. Due to advances in data-collecting techniques, RUL prediction based on the degradation data has attracted considerable attention during the past decade. However, the previous studies have mainly focused on the Weiner process as the degradation model, which has been shown inadequate for many real datasets, especially when the degradation path is monotone. On the other hand, the inverse Gaussian (IG) process has been shown as a popular alternative to the Wiener process. Despite the importance of IG process in degradation modelling, however, there remains a paucity of studies on the RUL prediction based on the IG process. Therefore, the principal objective of this study is to provide a systematic analysis of the RUL prediction based on the IG process. We first propose a series of novel online estimation algorithms so that the model parameters can be efficiently updated whenever a new collection of degradation measurements is available. The distribution of RUL is then derived, which could also be recursively updated. In view of the possible heterogeneities among different systems, we further extend the proposed online algorithms to the IG random-effect model. Numerical studies and asymptotic analysis show that both the parameters and the RUL can be efficiently and credibly estimated by the proposed algorithms. At last, two real degradation datasets are used for illustration.

主讲人简介：

徐安察，博士，浙江工商大学统计学教授，博士生导师，入选“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术带头人。担任《Statistical Theory and Related Fields》期刊Associate Editor，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面上项目2项、青年项目1项，浙江省自然科学基金重点项目1项，其他省部级项目4项，在IEEE TR、RESS、CIE、CSDA、JSPI等期刊上发表论文50多篇，获第一届全国统计科技进步奖三等奖、福建省自然科学奖三等奖。