A novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder for small negative samples

Abstract

Abstract This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder (DCAE) for small negative samples. The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning. In order to reduce the high cost of training Deep Neural Networks, this paper pre‐trained the Convolutional Neural Networks (CNN) through open labelled datasets. Through transferring learning, the encoder part of the traditional Convolutional Auto‐Encoder was replaced by the first three layers of the CNN, and a small number of defect samples were used to fine‐tune the parameters. A threshold discrimination scheme was designed to evaluate the model detection, realising the self‐explosion detection of insulator by judging the residual result and abnormal score. The experimental results show that compared with the existing insulator self‐explosion detection schemes, the proposed scheme can reduce the model training time by up to 40%, and the recognition accuracy can reach 97%. Moreover, this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.