Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval

<p dir="ltr">Fault detection and diagnosis (FDD) systems play a crucial role in maintaining the adequate execution of the monitored process. One of the widely used data-driven FDD methods is the Principal Component Analysis (PCA). Unfortunately, PCA’s reliability drops when data has...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohammed Tahar Habib Kaib (21633176) (author)
Other Authors:	Abdelmalek Kouadri (21633179) (author), Mohamed-Faouzi Harkat (16869897) (author), Abderazak Bensmail (21633182) (author), Majdi Mansouri (16869885) (author)
Published:	2024
Subjects:	Engineering Control engineering, mechatronics and robotics Information and computing sciences Machine learning Fault detection and diagnosis (FDD) data-driven techniques time and storage space complexity principal component analysis (PCA) kernel principal component analysis (KPCA) reduced KPCA (RKPCA) histogram cement plant three tanks system Principal component analysis Kernel Training data Fault detection Covariance matrices Histograms Loading Data models Cement industry Kilns
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Improvement of Kernel Principal Component Analysis-Based Approach for Nonlinear Process Monitoring by Data Set Size Reduction Using Class Interval

Similar Items