Anomaly Detection of Electro-data Based on Deep Convolutional Neural Network

As diversity of electro-data anomaly, the methods based on artificial feature are becoming more difficult to detect anomalies among a great deal of electro-data. Hence, this paper proposes a novel method which is based on deep convolutional neural network (DCNN) to detect anomaly electro-data. This method models the sample data with time information and electrical parameters, and labels them as normal or abnormal automatically. Further, the paper improves the designing DCNN to extract precise features from large scale of electro-data to get high accuracy. The results of the case analysis show that our method can detect anomaly electro-data more exact and stable than the traditional methods. The abnormal precision rate and abnormal recall rate of our approach reach 92.7% and 91.3% respectively.