Detection of Insulator Faults in Catenary Systems with Deep Learning

Abstract

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.

Ray Yüzey Kusurları ve Ray Kırıklarının Evrişimli Artık Ağ Modeli ile Sınıflandırılması

“…Demiryolu bakım konuları içinde sadece ray kusurları değil farklı konularda da ResNet kullanılan makaleler literatürde mevcuttur. Örneğin katener sistemlerindeki izolatör kusurlarının derin öğrenme ile tespiti ile ilgili bir çalışmada ResNet-34 mimarisi kullanılmış ve 1100 adet izolatör görüntüsü içeren veri seti ile %95'in üzerinde bir sınıflandırma başarısı elde edilmiştir [19].…”

Intelligent fault diagnosis of railway pantograph using a novel graph construction methodology

“…With the development of deep learning algorithm and it has been widely applied to fault diagnosis of a critical component in railway vehicles, including pantographs. Karaduman et al [14] proposed a pantographcatenary arc detection method based on a convolutional neural network. Lin et al [15] used the improved yolov3 model to realize the electrical fault identification of the pantograph.…”