Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
