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In pantographs, current collector strips transmit the electrical energy they receive from the catenary to the locomotive and provide the necessary power for the locomotive's movement. In order for the current collector strips to transmit electricity to the locomotive in a healthy way, their surface must be smooth. Wear on the surface of the current collector strips reduces conductivity and can create arcs, endangering the health and safety of the pantograph and catenary system. In this paper, a Convolutional Neural Network (CNN) architecture is developed to detect wear on the current collector strips. Images obtained from pantographs used on railways were created with a clean and improved data set using Hough Transform and Power Law Transform. The created dataset contains 909 pantograph images. This dataset was trained and tested with both the developed CNN architecture and classic deep learning architectures (ResNet50, VGG16). The experimental results show that the developed CNN architecture's training results and test results are more successful than classical architectures.

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Insulators are the most important components of catenary systems in electrified railway lines. Fractures or burns in insulators cause interruptions in transportation. These interruptions also prevent safe operation, especially on high-speed rail lines. Detecting faults in insulators at an early stage will enable to intervene in catenary systems at the most appropriate time and prevent insulator-related accidents. In this article, a deep learning-based method is proposed to classify insulators in catenary systems as faulty or intact. A data set containing 1100 isolator images was used in the study. The images in this dataset are trained and tested with the ResNet34 deep learning architecture. With the proposed architecture, faults in isolators are classified with 95,7% accuracy, 99% precision and 96,6% recall values. These values show that the performed study is a reliable method for fault detection in isolators in catenary systems.