Defect Detection of Pantograph Slide Based on Deep Learning and Image Processing Technology

Abstract

Pantograph is one of the most important components in electrical railway vehicles. To guarantee steady power supply for the train, the surface of the pantograph slide plate should be smooth enough so that the catenary can move on it from one side to the other side steadily with low friction. In addition, the thickness of the pantograph slide plate cannot be smaller than the lower limit for the sake of safety. Therefore, periodical inspection and maintenance of the pantograph slide plate are significant in terms of safe and stable operation. In this paper, an innovative and intelligent method based on deep learning and image processing technologies is proposed for the online condition monitoring of the pantograph slide plate. In the first stage, the surface defect detection and recognition method of the pantograph slide plate is proposed. Four typical surface defects of the slide are considered, and a deep learning model, pantograph defect detection neural network (PDDNet), is trained for the defect detection and recognition. In the second stage, five key criteria for qualifying the wear condition are proposed. The wear edge estimation based on image processing technology is investigated in detail. Furthermore, they are used to calculate the wear depth and evaluate the wear condition of the pantograph slide. The experiment results demonstrate that the proposed PDDNet can detect the surface defects and also recognize the four kinds of defects with a sound accuracy. The wear depth estimation results are compared with on-site measurement data, and the proposed method can achieve high estimation accuracy.