Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 49, Issue 17, Pages 7849-7857Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ie9018947
Keywords
-
Categories
Funding
- Roberto Rocca Education Program
- Texas Wisconsin California Control Consortium
Ask authors/readers for more resources
This paper presents a new method for fault diagnosis based on kernel principal component analysis (KPCA). The proposed method uses reconstruction-based contributions (RBC) to diagnose simple and complex faults in nonlinear principal component models based on KPCA. Similar to linear PCA, a combined index, based on the weighted combination of the Hotel ling's T-2 and SPE indices, is proposed. Control limits for these fault detection indices are proposed using second-order moment approximation. The proposed fault detection and diagnosis scheme is tested with a simulated CSTR process where simple and complex faults are introduced. The simulation results show that the proposed fault detection and diagnosis methods are effective for KPCA.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available