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Abdulrahman Youssef
Researcher at CentraleSupélec
Publications - 6
Citations - 163
Abdulrahman Youssef is an academic researcher from CentraleSupélec. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 5, co-authored 6 publications receiving 118 citations. Previous affiliations of Abdulrahman Youssef include Université Paris-Saclay & Supélec.
Papers
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Journal ArticleDOI
An optimal fault detection threshold for early detection using Kullback-Leibler Divergence for unknown distribution data
TL;DR: An incipient fault detection method that does not need any a priori information on the signals distribution or the changed parameters is proposed and an analytical model of the fault detection performances (False Alarm Probability and Missed Detection Probability) is developed.
Journal ArticleDOI
Kullback-Leibler Divergence for fault estimation and isolation : Application to Gamma distributed data
TL;DR: In this article, a fault detection, isolation and estimation method based on data-driven approach was developed for feature extraction and feature analysis using statistical techniques, where the Principal Component Analysis (PCA) method was used to extract the features and to reduce the data dimension.
Proceedings ArticleDOI
Performances Theoretical Model-Based Optimization for Incipient Fault Detection with KL Divergence
TL;DR: An analytical model of the fault detection performances (False Alarm Probability and Miss Detection Probability) based on the KLD including the noisy environment characteristics is developed and an optimization procedure is applied to set the optimal fault detection threshold depending on the SNR and the fault severity.
Proceedings ArticleDOI
Capability evaluation of incipient fault detection in noisy environment: A theoretical Kullback-Leibler Divergence-based approach for diagnosis
TL;DR: This paper is an attempt to determine an analytical model allowing to estimate a theoretical threshold for fault detection based on the Fault to Noise Ratio (FNR), developed using the Kullback-Leibler Divergence (KLD).
Proceedings ArticleDOI
Analytical model of the KL divergence for gamma distributed data: Application to fault estimation
TL;DR: An analytical model of the Kullback-Leibler Divergence for Gamma distributed data to be used for the fault severity estimation and shows that for incipient faults (<;10%) in usual noise conditions (SNR>40dB), the analytical model is accurate enough with a relative error around 10%.