学术报告通知
报告人：尚超 清华大学博士
时间：2018年12月10日（周一） 14:30~15:30
地点：实验十五楼207室
题目：Process Data Analytics Induced by Slowness Principle
欢迎各位老师同学参加！

报告摘要：

    Safe and steady operations of complex industrial processes are of significant importance to maximize economic and social benefits. Data-driven methods are suitable for modeling complicated industrial processes, and thus have been widely applied to areas such as process monitoring, fault diagnosis, and quality prediction. However, traditional data-driven models fail to make full use of process data. Their physical meanings are far from interpretable, thereby leaving a large quantity of information unused. This leads to a series of practical limitations, such as high false alarm rates and low quality prediction accuracies, thereby increasing economic costs and potential safety risks in the process industry.
    In this talk, we introduce a tailored process data analytics framework arising from slowness principle, which is in line with the spirit of representation learning and shapes a systematic data-driven paradigm. Its exclusive advantage will be illustrated via various applications in process monitoring, fault diagnosis, control performance assessment and quality predictions. In particular, primary attention will be placed on a holistic design of intelligent alarm systems for process industries. With temporal behaviors of industrial processes explicitly interpreted, the developed model enables effective discriminations between nominal operating condition deviations and process dynamics anomalies. It helps deciding whether the process model should be updated in an automatic way, and hence reduces manual labor and ensures the long-running of industrial processes.

报告人简历： 尚超，博士，现任清华大学自动化系助理教授、博士生导师，主要研究领域为数据驱动的建模、监控、控制与优化方法，以及在过程能源系统工程中的应用。尚超于2011年本科毕业于清华大学自动化系，2016年获得清华大学控制科学与工程博士学位，其中于2014年10月-2015年6月在加拿大阿尔伯塔大学进行联合培养，2016年10月-2018年10月在美国康奈尔大学与清华大学从事博士后研究工作。目前累计发表SCI论文十余篇，被引用360余次，已授权国家发明专利4项，所获荣誉包括清华大学“紫荆学者”称号、北京市优秀毕业生、清华大学优秀毕业生等，其博士论文被评为德国Springer出版社优秀博士论文奖以及清华大学优秀博士论文一等奖。