​​​​​​​We were glad to have @AA from the Clemson University ​​​​​​​present at the 5th Wind & Drivetrain Conference 2022 on April 7, 2022,.

Abdelrahman joined the Mechanical Engineering Department as a Ph.D. student and a Graduate Research Assistant in January 2020. He received his B.Sc. from the American University in Cairo (AUC) in 2014. 
Abdelrahman then spent the next three years working as a Design Release Engineer at General Motors, Egypt. He joined the Automotive Engineering Department at Clemson University in 2017, where he received his M.Sc. in 2019 with a focus on vehicle dynamics and noise, vibration, and harshness. 
His Ph.D. research is directed toward vibration-based condition monitoring in the wind turbine gearbox.

Abstract:

A significantly increased production of wind energy offers a path to achieving the goals of green energy policies in the United States and other countries. However, failures in wind turbines, specifically their gearboxes, are higher due to their operation in unpredictable wind conditions resulting in downtime and losses. Improved and early detection of faults in wind turbines will significantly increase their reliability and commercial feasibility. Because of their powerful feature learning capacity, data-driven fault diagnosis techniques based on deep learning have recently gained increased attention. However, one of the critical challenges is performing this diagnosis on wind turbines that inherently run under time-varying operating conditions. Other signal components that are not of interest and high noise levels mask the signal components or signature generated by incipient damage. This presentation proposes a deep learning-based fault diagnosis method based on bivariate cyclic spectral coherence and cartogram images to improve the recognition performance of wind turbine gearbox faults. Thus, these two types of 2D map representations are used to train convolutional neural networks.

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Multibody Dynamics Multibody Simulation Motion Multibody System Simulation Wind Turbine Dynamics Wind Turbines Simpack Drivetrain Wind Turbine Engineering