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 characterist...

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Autori principali:	Mohammed Tahar Habib Kaib, Abdelmalek Kouadri, Mohamed-Faouzi Harkat, Abderazak Bensmail, Majdi Mansouri
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2024-01-01
Serie:	IEEE Access
Soggetti:	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)
Accesso online:	https://ieeexplore.ieee.org/document/10401163/
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