Akademik Veri Yönetim Sistemi
International Research in Engineering Sciences X, Prof. Dr. Servet Soygüder, Editör, Eğitim Kitabevi, Konya, ss.28-38, 2024
Abstract: This study provides an exhaustive overview of the implementation
of Convolutional Neural Networks (CNNs) in the domain of electrical machines.
Beginning with an introduction to CNNs, the study discusses their fundamental
concepts and architecture, highlighting their suitability for analyzing complex
data patterns in electrical systems. Following this, an overview of electrical
machines and their diverse applications is presented, emphasizing the need
for advanced analytical tools like CNNs to address modern challenges in fault
detection, condition monitoring, and predictive maintenance. The study then
delves into the specific challenges encountered in implementing CNNs in
electrical machines, ranging from data scarcity and preprocessing complexities
to architectural design considerations and computational constraints. Strategies
for data preprocessing tailored to CNN applications in electrical machines
are discussed, covering techniques for data augmentation, normalization, and
feature extraction. Subsequently, the study explores architectural design and
optimization approaches for CNN models, focusing on parameter tuning,
network depth, and regularization techniques to enhance model performance
and efficiency. The crucial phases of training and testing CNN models for
electrical machines are elaborated upon, encompassing methodologies for
hyperparameter tuning, model evaluation, and performance comparison.
The study concludes by outlining future directions and emerging trends in
CNNs for electrical machines, including the integration of multi-modal data,
advanced model architectures, and ethical considerations. Finally, examples
of CNN applications in electrical machines are presented, showcasing real
world implementations and demonstrating the transformative potential of
CNN technology in enhancing the reliability, efficiency, and safety of electrical
systems.