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• W3133723227 abstract "Fault detection (FD) is fundamental for monitoring several chemical processes. Thus, this paper introduces a novel structure multiscale reduced kernel principal component analysis (MS-RKPCA). The proposed FD method aims to address the problem of great computation time and significant storage memory space by using a data reduction structure based on the Euclidean distance metric. Additionally, to further enhance the RKPCA method, a multiscale representation of data will be used. The enhanced MS-RKPCA method uses the wavelet coefficients of the reduced data at each scale to enhance the fault detection results. The detection performance of the proposed MS-RKPCA method is evaluated using the Tennessee Eastman Process (TEP). The effectiveness of the enhanced method is evaluated in terms of the missed detection rates (MDR), false alarms rates (FAR) and computation time (CT). The results demonstrate that the developed technique is more effective for fault detection mostly in terms of computation time and memory storage space." @default.
• W3133723227 created "2021-03-15" @default.
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• W3133723227 date "2020-07-20" @default.
• W3133723227 modified "2023-10-05" @default.
• W3133723227 title "Machine Learning based Multiscale Reduced Kernel PCA for Nonlinear Process Monitoring" @default.
• W3133723227 cites W1587559447 @default.
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• W3133723227 doi "https://doi.org/10.1109/ssd49366.2020.9364160" @default.
• W3133723227 hasPublicationYear "2020" @default.
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