DOI: 10.1007/3-540-32390-2_103
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Pattern Recognition and Fault Detection in MEMS

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Cited by 12 publications
(1 citation statement)
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“…A multivariate FD method take into account the correlation between the process variables while univariate FD methods do not. Multivariate statistical monitoring methods include the latent variable methods, e.g., partial least square (PLS) regression, principal component analysis (PCA), canonical variate analysis (CVA), independent component analysis (ICA), (Chaing et al (2001); Venkatasubramanian et al (2003b)), neural networks (Subbaraj and Kannapiran (2010)), Fuzzy systems (Dexter and Benouarets (1996)) as well as the pattern recognition methods (Mohammadi and Asgary (2005)). …”
Section: The State Of the Artmentioning
confidence: 99%
“…A multivariate FD method take into account the correlation between the process variables while univariate FD methods do not. Multivariate statistical monitoring methods include the latent variable methods, e.g., partial least square (PLS) regression, principal component analysis (PCA), canonical variate analysis (CVA), independent component analysis (ICA), (Chaing et al (2001); Venkatasubramanian et al (2003b)), neural networks (Subbaraj and Kannapiran (2010)), Fuzzy systems (Dexter and Benouarets (1996)) as well as the pattern recognition methods (Mohammadi and Asgary (2005)). …”
Section: The State Of the Artmentioning
confidence: 99%