Bushings are served as an important component of the power transformers; it's of great significance to keep the bushings in good insulation condition. The infrared images of the bushing are proposed to diagnose the fault with the combination of image segmentation and deep learning, including object detection, fault region extraction, and fault diagnosis. By building an object detection system with the frame of Mask Region convolutional neural network (CNN), the bushing frame can be exactly extracted. To distinguish the fault region of bushings and the background, a simple linear iterative clustering-based pulse coupled neural network is proposed to improve the fault region segmentation performance. Then, two infrared image feature parameters, the relative position and area, are explored to classify fault type effectively based on the K-means cluster technique. With the proposed joint algorithm on bushing infrared images, the accuracy reaches 98%, compared with 44% by the conventional CNN classification method. The integrated algorithm provides a feasible and advantageous solution for the field application of bushing image-based diagnosis.

套管是电力变压器的重要组成部分。保持套管良好的绝缘状态非常重要。提出将套管的红外图像结合图像分割和深度学习技术进行故障诊断，包括目标检测，故障区域提取和故障诊断。通过使用“蒙版区域”卷积神经网络（CNN）框架构建对象检测系统，可以精确提取套管框架。为了区分套管的故障区域和背景，提出了一种基于线性迭代聚类的简单脉冲耦合神经网络，以提高故障区域的分割性能。然后，两个红外图像特征参数，相对位置和面积，基于K-means聚类技术，探索有效地对故障类型进行分类。与常规的CNN分类方法相比，该方法对套管红外图像提出了联合算法，其准确率达到了98％。该集成算法为基于套管图像的诊断的现场应用提供了可行且有利的解决方案。