Abstract
Abstract In this paper, a new data-driven method called just-in-time learning canonical correlation analysis (JITL-CCA) for tackling nonlinearity in process monitoring is proposed. Canonical correlation analysis (CCA)-based fault detection method has been applied for linear static and dynamic processes. However, CCA has deficiency in coping with nonlinearity existing in real applications, as with other well-established multivariate analysis techniques. This deficiency is illustrated by a numerical example. In recent years, nonlinear analysis tools using kernel principles have been proposed. But the main problem lies in the parameter of kernel function is sensitive and difficult to select. This paper constructs JITL-CCA method to realize on-line learning and monitoring, to build local model and to detect faults with simple parameter setting. Based on T²statistic in the feature space, JITL-CCA is validated by the simulation benchmark of CSTR.
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.
