讲座时间：2025年10月16日(周四) 14：00
地点： 综合楼644会议室
讲座一：Statistical Monitoring of Incomplete Data from Censored Life Testing
主讲人：肖逊
报告人简介：
Dr. Xun Xiao is currently a Senior Lecturer at the Dept. of Mathematics and Statistics, University of Otago, New Zealand. He received B.Sc. in Statistics from the University of Science and Technology of China in 2011 and Ph.D. degree from the Dept. of Systems Engineering and Engineering Management at City University of Hong Kong in 2016. His current research focuses on industrial statistics and point process modelling.
报告摘要：
Life tests for highly reliable products often take a long time, even when using accelerated life testing with censoring. When the production process is monitored with lifetime as the key quality characteristic, the time spent on life testing can result in significant delays for practitioners in making decisions after sampling. However, shortening the test duration, which leads to excessive right-censored observations, inevitably reduces the test power for anomaly detection. In this talk, we will investigate the optimal design of censoring time in life tests when monitoring lifetime data with likelihood-based control charts. Both finite-sample analytical and large-sample asymptotic results are examined for type-I censored exponential lifetimes. We further generalize the results to other common lifetime distributions, including Weibull, lognormal, and gamma distributions. Our investigation uncovers the twofold impact of censoring time on the actual performance of control charts under various scenarios and provides valuable references for practitioners in setting more reasonable censoring times in life testing on real-world production lines.
讲座二：Enhancing Battery RUL Prediction: Regeneration Modeling & Domain Robustness
主讲人：陈飘
报告人简介：
Dr. Piao Chen is currently an associate professor at the ZJUI Institute, Zhejiang University. He previously served as an assistant professor in statistics at TU Delft, the Netherlands. His research interests include quality and reliability, statistical learning, and decision optimization. He has published over 30 papers in leading journals across management, engineering, and statistics, such as Management Science, Production and Operations Management, and IEEE Transactions on Information Theory. His work has received several Best Paper Awards at international conferences, including INFORMS QSR and SRSE.
报告摘要：
Reliable remaining useful life (RUL) prediction of lithium‑ion batteries underpins proactive maintenance and lifecycle optimization. Two pervasive issues compromise prediction fidelity: (1) non‑monotonic capacity regeneration events, and (2) domain heterogeneity caused by batch‑to‑batch variations. This talk focuses on our dual contributions: a monotone decomposition technique that segregates the capacity signal into a monotonically decreasing component and a regeneration term, each forecasted via Gaussian processes and deep autoregression for uncertainty‑aware RUL estimates; and a robust transfer‑learning ensemble that leverages early‑cycle kernel regression with domain‑distance–based weighting and transfer component analysis for cross‑batch alignment. Results on multiple datasets demonstrate the efficacy of our approaches in real‑world scenarios.
讲座三： Statistical Modeling and Reliability Analysis for Degradation Processes Indexed by Two Scales
主讲人：翟庆庆
报告人简介：
翟庆庆，上海大学管理色情网址大全副教授，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，FEM特约通讯专家等。2015年于北京航空航天大学获系统工程专业博士学位。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数据建模、系统可靠性等。在JASA、Technometrics、IISE Transactions、ITR、RESS等期刊发表论文50余篇，出版中英文专著3部，曾获IEEE工业电子协会最佳论文奖等。
报告摘要：
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 is justified 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.
