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    Investigation of Fault Modeling in the Identification of Bearing Wear Severity 

    Source: Journal of Tribology:;2021:;volume( 144 ):;issue: 007:;page 71802-1
    Author(s): Alves, Diogo Stuani; Machado, Tiago Henrique; da Silva Tuckmantel, Felipe Wenzel; Keogh, Patrick S.; Cavalca, Katia Lucchesi
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Recent research into machines involved in power generation processes has demanded deep investigation of model-based techniques for fault diagnosis and identification. The improvement of critical fault characterization is ...
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    Verification and Validation of Rotating Machinery Using Digital Twin 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2024:;volume( 010 ):;issue: 001:;page 11104-1
    Author(s): Yanik, Yasar; Ekwaro-Osire, Stephen; Dias, João Paulo; Porto, Edgard Haenisch; Alves, Diogo Stuani; Machado, Tiago Henrique; Bregion Daniel, Gregory; de Castro, Helio Fiori; Cavalca, Katia Lucchesi
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Rotating machinery has extensive usage in industrial applications, either as leading equipment (power plants) or as auxiliary equipment (oil and gas exploitation). These highly complex systems demand expensive maintenance ...
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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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