Nonlinear Principal Component Analysis, used to extract linear and non-linear correlations between variables, and the non-linear principal component analysis method based on neural networks for the estimation of non-linear components linear. The detection of defects is carried out by the statistical method SPE (square prediction error), and the location of defects by calculating contributions.
Dear Lotfi,
Please find the following papers using neuronal NLPCA for process modeling and monitoring:
Article Sensor fault detection, isolation and reconstruction using n...
Article New nlpca model combining neuronal principal curves and rbf ...
Regards
Dear Mhd Fawzi haraket,
Thank you very much. We strongly appreciated your comment and your paper.
Best regards.
If someone is interested in my related question: https://www.researchgate.net/post/Principal_Component_Analysis_or_something_alike_for_nonlinear_Parameter_estimation
NLPCA used to extract linear and non-linear correlations between variables well in conjunction with other technique and NLPCA-NN can use after considerable time of calculation for the estimation of NLCP. The detection of defects is carried out by the statistical method SPE (not in all case ).
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