标题:Two-step dynamic slow feature analysis for dynamic process monitoring
作者:Si, Yabin ;Wang, Youqing
作者机构:[Si, Yabin ] College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China;[Wang, Youqing ] College of Elec 更多
会议名称:1st International Conference on Industrial Artificial Intelligence, IAI 2019
会议日期:22 July 2019 through 26 July 2019
来源:1st International Conference on Industrial Artificial Intelligence, IAI 2019
出版年:2019
DOI:10.1109/ICIAI.2019.8850780
关键词:Dynamic process monitoring; Dynamic SFA (DSFA); Slow feature analysis (SFA); Two-step DSFA (TS-DSFA)
摘要:Many successful classical multivariate statistical process monitoring (MSPM) approaches have been applied in industrial processes. However, most of these methods and their extended dynamic versions fail to distinguish real faults incurring dynamic anomalies from normal changes in operating conditions in process dynamics. One popular solution is based on slow feature analysis (SFA) and dynamic SFA (DSFA). Notice that SFA and DSFA use a pair of statistics for monitoring dynamic processes without considering dynamic structure. In this study, a two-step DSFA (TS-DSFA) is proposed for monitoring dynamic processes. TS-DSFA firstly separates dynamic components from dynamic processes, and then constructs a evaluation model of dynamic processes. TS-DSFA assists in distinguishing real faults from normal changes in operating conditions, and it shows good performance in monitoring dynamic processes with uncertain noises. Finally, a numerical case is presented to verify the effectiveness of the TS-DSFA. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073560018&doi=10.1109%2fICIAI.2019.8850780&partnerID=40&md5=545282af217d9cc1eee1e1d6635740e5
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