Abstract
Multivariate statistical process monitoring has been widely used in industry. However, traditional algorithms often ignore the dynamic characteristics of actual industry process. This study proposes a novel algorithm called multistep dynamic slow feature analysis (MS-DSFA), which has completed the full-condition monitoring of a dynamic system and divided dynamic structures more precisely. This algorithm achieves an optimal detection rate according to multiple control limits. To enrich the experiments, we select a numerical example, Tennessee Eastman process, and XJTU-SY bearing data sets to verify the universality of the algorithm. According to the overall score for optimal detection rates and false alarm rates, MS-DSFA stands out in the comparison of existing algorithms.
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 callMore From: IEEE Transactions on Instrumentation and Measurement
Disclaimer: 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.
