报告人：IEEE Fellow、IFAC Fellow、南加州大学秦泗钊教授 
时间：2019年1月9日（今天）下午15:20-17:00
地点：研究生楼906
题目：Process data analytics for troubleshooting of feedback controlled manufacturing plants

Abstract:
    Although manufacturing process systems collect and store massive amount of data from routine operations with computer control systems, most control theory and practice research to date have focused on either system identification where the data are collected with carefully designed experiments or on fault detection where the normal process models are assumed to be available. It is also clear that many processes have poor control performance and often exhibit dynamic oscillations, albeit over a century of stability theory exists. We make a proposition that these undesirable performances are due to uncertainty and abnormal situations that develop during routing operations and go beyond the capability of normal models. We further assert that routing data contain up to date situational knowledge about the process performance and abnormal situational knowledge, that can be effectively mined by properly analyzing operation data. Since the massive operation data are usually dynamic but are far from being fully excited, theory and methods are needed to analyze these data where the dynamics exist only in a subspace of the high dimensional measurement space.
    In this talk we first provide a historical perspective on the process data analytics based on latent variables modeling methods and machine learning, and the objectives to distill desirable components or features from measured data under routine operations. These methods are then extended to modeling high dimensional dynamic time series data to extract the most dynamic latent variables. We show with an industrial case study how real process data are efficiently and effectively modeled using these dynamic methods to extract features for process operations and control, leading to new perspectives on how process data are indispensable for manufacturing process troubleshooting, diagnosis, and effective control.

Bio:
    S. Joe Qin obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, in 1984 and 1987, respectively, and his Ph.D. degree in ChemicalEngineering from University of Maryland at College Park in 1992. His research interests include statistical process monitoring, process data analytics, machine learning and big data, model predictive control, system identification, building energy optimization, and control performance monitoring. He has published over 300 journal and conference papers, with a Web of Science h-index of 49.
    Dr. Qin was a Director of the AIChE CAST Division (2003-2006), Member of the AIChE International Committee (2011-2014), and Editor AIChE Chemical Engineering Faculty Directory (2000-2007). He has been elected as a Fellow of IEEE and Fellow of the International Federation of Automatic Control (IFAC). He is currently a Subject Editor for Journal of Process Control and a Member of the Editorial Board for Journal of Chemometrics.