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Abdelmalek Kouadri
Researcher at University of Boumerdes
Publications - 50
Citations - 799
Abdelmalek Kouadri is an academic researcher from University of Boumerdes. The author has contributed to research in topics: Fault detection and isolation & Computer science. The author has an hindex of 12, co-authored 45 publications receiving 387 citations. Previous affiliations of Abdelmalek Kouadri include Texas A&M University at Qatar.
Papers
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Journal ArticleDOI
Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems
Abdelmalek Kouadri,Abdelmalek Kouadri,Mansour Hajji,Mohamed-Faouzi Harkat,Kamaleldin Abodayeh,Majdi Mansouri,Hazem Nounou,Mohamed Nounou +7 more
TL;DR: An advanced FDD approach is presented that exploits the benefits of the machine learning (ML)-based Hidden Markov model (HMM) and the principal component analysis (PCA) model to increase the availability, reliability and required safety of WEC Converters (WECC) under different conditions.
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Reduced Kernel Random Forest Technique for Fault Detection and Classification in Grid-Tied PV Systems
Khaled Dhibi,Radhia Fezai,Majdi Mansouri,Mohamed Trabelsi,Abdelmalek Kouadri,Kais Bouzara,Hazem Nounou,Mohamed Nounou +7 more
TL;DR: This article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF$_{\text{ED}}$) and K-means clustering based reduced Kernel RF, for FDD.
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A new adaptive PCA based thresholding scheme for fault detection in complex systems
TL;DR: In this article, a new adaptive thresholding scheme based on a modified exponentially weighted moving average (EWMA) control chart statistic was proposed to detect small changes and abrupt shifts in the process operation.
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Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems
Mansour Hajji,Mohamed-Faouzi Harkat,Mohamed-Faouzi Harkat,Abdelmalek Kouadri,Abdelmalek Kouadri,Kamaleldin Abodayeh,Majdi Mansouri,Hazem Nounou,Mohamed Nounou +8 more
TL;DR: In the proposed FDD approach, the principal component analysis (PCA) technique is used for extracting and selecting the most relevant multivariate features and the supervised machine learning (SML) classifiers are applied for faults diagnosis.
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Fault detection and diagnosis in a cement rotary kiln using PCA with EWMA-based adaptive threshold monitoring scheme
TL;DR: In this paper, the authors presented the main results of fault detection and diagnosis in a cement manufacturing plant using a new monitoring scheme based on multivariate statistical analysis and an adaptive threshold strategy.