A Novel Multi-dimensional Time-series Data Anomaly Detection Model Based on Generative Adversarial Network Aided Autoencoder

Abstract

With increasing amount and easiness of access the data in industrial processes, data-driven technologies have become more prevalent in process monitoring. Anomaly detection is an indispensable part of process monitoring. However, most industrial data are closely related to time, and classical anomaly detection algorithms mostly focus on learning the features of static data, ignoring the dynamic features of industrial data. This paper proposes a multi-dimensional time-series data anomaly detection model based on generative adversarial network aided autoencoder. By extracting the features of the normal time-series data, feature representation is established in latent space. Meanwhile, we introduce generated adversarial network (GAN) into the autoencoder (AE) training to enhance the feature learning ability of the autoencoder, so that the normal time-series data can be well represented in the latent space. Gated recurrent unit (GRU) is used as the main network of the autoencoder to learn the dynamic features between different time steps in the sequence data and detect fault data through the value of the reconstruction error. We verify the validity of the proposed model in simulation data and apply it to the real anomaly detection of steel plate production. Compared with k-nearest neighbor, linear discriminant analysis, principal component analysis, one-class support vector machine, data-enhanced method and the traditional dynamic autoencoder, the proposed method performs the best.