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

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.