12月8日中心“工业统计方法”系列讲座预告

主题一：A Subsampling Method for Regression Problems Based on Minimum Energy Criterion

主讲人简介：

王典朋，北京理工大学特别副研究员，博士生导师。王典朋副研究员在北京理工大学获得博士学位，中国科学院数学与系统科学研究院博士后，曾先后访问佐治亚理工、香港科技大学，担任北京大数据协会常务理事、中国现场统计研究会试验设计分会理事。主要从事敏感性试验设计、计算机试验设计、贝叶斯计算、等方向的研究。主持国家自然科学基金青年基金、面上项目和国家国防科技工业局先进星箭共性技术等项目多项，在Technometrics、Journal of Quality Technology、Statistica Sinica、RESS、Applied Mathematical Modelling等统计学权威期刊上发表论文多篇。

讲座摘要：

The extraordinary amounts of data generated nowadays pose heavy demands on computational resources and time, which hinders the implementation of various statistical methods. An efficient and popular strategy of downsizing data volumes and thus alleviating these challenges is subsampling. However, the existing methods either rely on specific assumptions for the underlying models or acquire partial information from the available data. For regression problems, we propose a novel approach, termed adaptive subsampling with the minimum energy criterion (ASMEC). The proposed method requires no explicit model assumptions and “smartly” incorporates information on covariates and responses. ASMEC subsamples possess two desirable properties: space-fillingness and spatial adaptiveness. We investigate the limiting distribution of ASMEC subsamples and their theoretical properties under the smoothing spline regression model. The effectiveness and robustness of the ASMEC approach are also supported by a variety of synthetic examples and two real-life examples.

主题二：Robust condition-based production and maintenance planning for degradation management

讲座地点：综合楼644会议室 

主讲人简介：

陈飘，浙江大学ZJU-UIUC联合学院副教授，在此之前担任代尔夫特理工大学统计学助理教授。 他于2013年在上海交通大学获得工业工程学士学位，并于2017年从新加坡国立大学获得工业与系统工程管理博士学位。他的研究主要关注质量和可靠性、故障诊断与健康管理以及统计学。他的大部分研究成果发表在统计、工程、管理领域的优秀期刊，包括Technometrics、Journal of Quality Technology、IEEE Transactions on Information Theory和Production and Operations Management等。曾获得国际系统可靠性与安全工程会议（SRSE2022）、INFORMS质量统计可靠性会议（ICQSR2023）、统计理论及其应用国际研讨会（STARF2023）等国际会议的最佳论文奖。

讲座摘要：

We study the robust production and maintenance control for a production system subject to degradation. A periodic maintenance scheme is considered, and the system production rate can be dynamically adjusted before maintenance, serving as a proactive way of degradation management. Optimal control of the degradation rate aims to strike a balance between the risk of failure and the production profit. We first consider the scenario in which the degradation rate increases linearly with the production rate. Different from the existing literature that posits a parametric stochastic degradation process, we suppose that the degradation increment during a period lies in an uncertainty set, and our objective is to minimize the maintenance cost in the worst case. The resulting model is a robust mixed-integer linear program. We derive its robust counterpart and establish structural properties of the optimal production plan. These properties are then used for real-time condition-based control of the production rate through reoptimization. The model is further generalized to the nonlinear production–degradation relation. Based on a real production–degradation dataset from an extruder system, we conduct comprehensive numerical experiments to illustrate the application of the model. Numerical results show that our model significantly outperforms existing methods in terms of the mean and variance of cost rate when degradation model misspecification is presented.

主题三：Statistical Modeling and Reliability Analysis for Degradation Processes Indexed by Two Scales

主讲人：翟庆庆

主讲人简介：

翟庆庆，上海大学管理学院副教授，上海市青年东方学者，担任中国现场统计研究会可靠性工程分会副秘书长，中国优选法统筹法与经济数学研究会工业工程分会理事。2015年于北京航空航天大学系统工程专业获得博士学位。2015年至2017年，在新加坡国立大学工业系统工程与管理系担任research fellow. 主要研究兴趣包括退化统计模型、可靠性建模和博弈论。在Technometrics、IISE Transactions、ITII、EJOR、ITR、RESS等国际期刊上发表论文40余篇。

讲座摘要：

Degradation is an important phenomenon for industrial products, which manifests as the gradually deterioration of some performance characteristics. The degradation process is often relevant to both time and usage, and indexing the degradation process merely by the time or usage cannot characterize the process accurately. Considering a stochastic usage process, this study proposes a degradation process model indexed by two scales, i.e., the time and the usage, where the degradation along the two scales are modeled as correlated nonlinear Wiener processes. We develop two simulation-based algorithms for reliability evaluation and study the model inference problems for the proposed model. The estimation procedure and the reliability assessment algorithms are validated by simulations. The performance of the proposed model isjustified with an application to a real degradation dataset of outdoor coating materials, which shows that indexing the degradation process by two scales can considerably improve the degradation modeling performance.