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

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

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 nonlinear characte...

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Bibliographic Details
Main Authors: Mohammed Tahar Habib Kaib, Abdelmalek Kouadri, Mohamed-Faouzi Harkat, Abderazak Bensmail, Majdi Mansouri
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
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)
Online Access:https://ieeexplore.ieee.org/document/10401163/

Internet

https://ieeexplore.ieee.org/document/10401163/

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