Abstract

Abstract
In this dissertation, we will introduce a novel methods of fault detection and isolation, this thesis is mainly dedicated to data driven fault detection and isolation updating in complex and uncertain processes.
Conventional Principal Component Analysis has good fault detection capability, however, it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing, particularly in complex and uncertain process. Hence, MSPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The proposed method adjusted with nonlinearity and the multimodal environment using sparsity to capture most of the variation of the three-way data.\\\\
Simulation results and comparing it with the state-of-the-art fault detection and isolation techniques demonstrate the methods’ effectiveness and efficiency.


Keywords
Fault Detection, Fault Diagnosis, Fault Isolation, Complex and Uncertain Process, Process monitoring, Principal Component Analysis, Sparse PCA.

Information

Item Type:	Thesis
Divisions:	» Laboratory of Electrical Engineering and Renewable Energy » Faculty of Science and Technology
ePrint ID:	4791
Date Deposited:	2024-01-16
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/4791

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