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    Deep Convolutional Neural Network Framework for Diagnostics of Planetary Gearboxes Under Dynamic Loading With Feature-Level Data Fusion 

    Source: Journal of Vibration and Acoustics:;2021:;volume( 144 ):;issue: 003:;page 31003-1
    Author(s): Gecgel, Ozhan; Ekwaro-Osire, Stephen; Gulbulak, Utku; Morais, Tobias Souza
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Planetary gearboxes are susceptible to premature failures due to cyclic random loadings and extreme operating conditions. Fault diagnostics strategies are crucial to increase operational safety and reduce economic costs. ...
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    Prognostics and Health Management of Wind Energy Infrastructure Systems 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2022:;volume( 008 ):;issue: 002:;page 20801-1
    Author(s): Yüce, Celalettin; Gecgel, Ozhan; Doğan, Oğuz; Dabetwar, Shweta; Yanik, Yasar; Kalay, Onur Can; Karpat, Esin; Karpat, Fatih; Ekwaro-Osire, Stephen
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute toward the prognostics and health management (PHM) ...
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    Simulation-Driven Deep Learning Approach for Wear Diagnostics in Hydrodynamic Journal Bearings 

    Source: Journal of Tribology:;2020:;volume( 143 ):;issue: 008:;page 084501-1
    Author(s): Gecgel, Ozhan; Dias, João Paulo; Ekwaro-Osire, Stephen; Alves, Diogo Stuani; Machado, Tiago Henrique; Daniel, Gregory Bregion; de Castro, Helio Fiori; Cavalca, Katia Lucchesi
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Early diagnosis in rotating machinery has been a challenge when looking toward the concept of intelligent machines. A crucial and critical component in these systems is the lubricated journal bearing, subjected to wear ...
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