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Fault detection in photovoltaic systems using machine learning technique., , , , , , and . SSD, page 207-212. IEEE, (2020)Fault Diagnosis for Dynamic Nonlinear System Based on Variable Moving Window KPCA., , , , and . SSD, page 590-595. IEEE, (2018)Uncertainty Quantification Kernel PCA: Enhancing Fault Detection in Interval-Valued Data., , , , , and . CoDIT, page 3021-3026. IEEE, (2024)Enhancing Fault Detection in Stochastic Environments Using Interval-Valued KPCA: A Cement Rotary Kiln Case Study., , , , and . IEEE Access, (2025)Combined Input Training and Radial Basis Function Neural Networks based Nonlinear Principal Components Analysis Model Applied for Process Monitoring., and . IJCCI, page 483-492. SciTePress, (2012)Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems., , , , , , and . Eur. J. Control, (2021)Fault detection localization and reconstruction in nonlinear system using RKPCA method and RBC., , , , and . ICCAD, page 487-492. IEEE, (2017)Machine Learning based Multiscale Reduced Kernel PCA for Nonlinear Process Monitoring., , , , , and . SSD, page 302-307. IEEE, (2020)Reduced Kernel Principal Component Analysis for Fault Detection and Its Application to an Air Quality Monitoring Network., , , , and . SMC, page 3159-3164. IEEE, (2018)Uncertain Dynamic Process Monitoring Using Moving Window PCA for Interval-Valued Data., , , , , and . DX, volume 2289 of CEUR Workshop Proceedings, CEUR-WS.org, (2018)