Fault identification for process monitoring using kernel principal component analysis
By Ji-Hoon Cho, Jong-Min Lee, S. Choi, Dongkwon Lee, In-Beum Lee. 2005
302 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/f7b08a8921c2b318517c378d86963d2f3b5fdf65
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In Analytical Chemistry
In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application (2013, background): “Hence, the necessary condition for the kernel function is to meet Mercer’s theorem [10, 27].”
- Applications of fault detection and diagnosis methods in nuclear power plants: A review (2011, unknown): “…and Kourti, 1995) (Wise and Gallagher, 1996), AAKR (Garvey and Hines, 2006), independent component analysis (ICA) (Hyvarinen, 1999), crosscalibration (Hashemian, 2006), and their variants (Kramer, 1991) (Qin and McAvoy, 1992) (Dong and McAvoy, 1996) (Schölkopf et al., 1998) (Cho et al., 2005).”
- Survey on data-driven industrial process monitoring and diagnosis (2012, methodology): “Kernel methods have received much attention in the last few years due to the kernel tricks that build a nonlinear latent variable model with roughly linear computation cost (Cho et al., 2005; Rosipal & Trejo, 2001; Willis, 2010).”