Intelligent Fault Diagnosis Tool for Power Line Systems

The paper focuses on the intelligent fault diagnosis system for automatic analysis of a huge amount of images with 4K resolution collected during flight inspection of the overhead power lines with the use of aerial platforms. The developed system can be used to detect and locate anomalies of power line structures e.g. anomalies of towers, conductors, dampers, insulators, etc. The purpose of the paper is to present the general architecture of the system and the results obtained during inspection of thousands of kilometer flights of power lines. The proposed system is based on the predefined deep neural network called Faster R-CNN which is dedicated for solving real-time object detection problems. The faster R-CNN-based neural model created on COCO dataset was additionally retrained by the authors applying images gathered as a result of flight inspections in order to obtain the high performance of the whole system. The comprehensive verification tests were carried out to prove the merits of the proposed solution.