Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning

Abstract

In view of the poor effect of most fault diagnosis methods on the intelligent recognition of equipment images, a fault diagnosis method of distribution equipment based on the hybrid model of robot and deep learning is proposed to reduce the dependence on manpower and realize efficient intelligent diagnosis. Firstly, the robot is used to collect the on-site state images of distribution equipment to build the image information database of distribution equipment. At the same time, the robot background is used as the comprehensive database data analysis platform to optimize the sample quality of the database. Then, the massive infrared images are segmented based on chroma saturation brightness space to distinguish the defective equipment images, and the defective equipment areas are extracted from the images by OTSU method. Finally, the residual network is used to improve the region-based fully convolutional networks (R-FCN) algorithm, and the improved R-FCN algorithm trained by the online hard example mining method is used for fault feature learning. The fault type, grade, and location of distribution equipment are obtained through fault criterion analysis. The experimental analysis of the proposed method based on PyTorch platform shows that the fault diagnosis time and accuracy are about 5.5 s and 92.06%, respectively, which are better than other comparison methods and provide a certain theoretical basis for the automatic diagnosis of power grid equipment.