M
Mohammed Mustafa
Researcher at Salman bin Abdulaziz University
Publications - 109
Citations - 575
Mohammed Mustafa is an academic researcher from Salman bin Abdulaziz University. The author has contributed to research in topics: Medicine & Fault detection and isolation. The author has an hindex of 10, co-authored 67 publications receiving 331 citations. Previous affiliations of Mohammed Mustafa include University of Tabuk & Luleå University of Technology.
Papers
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Journal ArticleDOI
Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines
George Georgoulas,Mohammed Mustafa,Ioannis Tsoumas,Jose A. Antonino-Daviu,Vicente Climente-Alarcon,Chrysostomos D. Stylios,George Nikolakopoulos +6 more
TL;DR: Results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.
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Detecting broken rotor bars in induction motors with model-based support vector classifiers
TL;DR: This work considers a model-based Support Vector Classification method for the detection of broken bars in three phase asynchronous motors at full load conditions, using features based on the spectral analysis of the stator's steady state current, including the amplitude of the lift sideband harmonic and the amplitude at fundamental frequency.
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A fault detection scheme based on minimum identified uncertainty bounds violation for broken rotor bars in induction motors
TL;DR: In this article, a method for broken bars fault detection in the case of three-phase induction motors and under different payloads is presented and experimentally evaluated, which is based on the Set Membership Identification (SMI) technique and a novel proposed minimum boundary violation fault detection scheme, applied on the identified motor's parameters.
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Synthesis of Silver Nanoparticles from Extracts of Wild Ginger (Zingiber zerumbet) with Antibacterial Activity against Selective Multidrug Resistant Oral Bacteria
Muhammad Ramzan,Mohmed Isaqali Karobari,Artak Heboyan,Roshan Noor Mohamed,Mohammed Mustafa,Syed Nahid Basheer,Vijay Desai,S Batool,Naveed Ahmed,Basit Zeshan +9 more
TL;DR: In this paper , the authors used wild ginger extracts to synthesize silver nanoparticles (AgNPs) and evaluated the antibacterial efficacy of these AgNPs against multidrug-resistant (MDR) Staphylococcus aureus, Streptococcus mutans, and Enterococcus faecalis.
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A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers
TL;DR: This study is proposed to review the sign language recognition system based on different classifier techniques, mainly the Neural Network and Deep Learning-based classifiers, and focused mainly on deep learning techniques and also on Arabic signlanguage recognition systems.