Feature Extraction Using Convolutional Neural Networks for Anomaly Detection

Anomaly detection is an import field of study, which has many applications, e.g., fraud and disease detection. It consists of identifying non-conforming patterns regarding an expected behavior. Despite the improvements provided by deep learning techniques in several areas, their use for anomaly detection is not widespread. The main reason is the difficulty to learn discriminative models when all the information available regards one class, or the classes are highly unbalanced. We propose a new deep learning-based solution for the anomaly detection problem. It consists of a hybrid system, composed of a feature extractor and a one-class classifier. The feature extractor is a convolutional neural network, trained as a regressor to learn a predefined distribution. The classifier is the one-class support vector machine, which performs the anomaly detection by using the outputs provided by the feature extractor. We used a gearbox failure diagnosis data set to assess the performance of our proposal. We also compared our anomaly detection system with other deep learning-based techniques commonly found in the literature. Our proposal presented an average accuracy close to 0.95, outperforming techniques based on the reconstruction error and hybrid models. Keywords— Anomaly Detection; Deep Learning; One-Class Support Vector Machine; Convolutional Neural Network