Abstract
For complex nonlinear systems of chemical industry process, traditional kernel principal component analysis (KPCA) methods are very difficult to calculate the kernel matrix for fault detection with large sample sets. So an improved fault detection method based on feature vector selection-KPCA (FVS-KPCA) is developed. This method can evidently reduce calculational complexity of fault detection and is applied to the benchmark of Tennessee Eastman (TE) processes. The simulation results show that the proposed method can effectively improve the speed of fault detection.
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.
