New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln
Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in
industrial process monitoring. It intends to promptly detect and identify abnormalities and
enhance the reliability and safety of the processes. Kernel Principal Component Analysis
(KPCA) is a powerful FDD based data-driven method. It has gained much interest due to its
ability in monitoring nonlinear systems. However, KPCA suffers from high computing time
and large storage space when a large-sized training dataset is used. So, extracting and …
industrial process monitoring. It intends to promptly detect and identify abnormalities and
enhance the reliability and safety of the processes. Kernel Principal Component Analysis
(KPCA) is a powerful FDD based data-driven method. It has gained much interest due to its
ability in monitoring nonlinear systems. However, KPCA suffers from high computing time
and large storage space when a large-sized training dataset is used. So, extracting and …
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