GAN-Based Anomaly Detection and Localization of Multivariate Time Series Data for Power Plant

Abstract

Recently, as real-time sensor data collection increases in various fields such as power plants, smart factories, and health care systems, anomaly detection for multivariate time series data analysis becomes more important. However, extracting significant features from multivariate time series data is still challenging because it simultaneously takes into account the correlation between the pair of sensors and temporal information of each time series. Meanwhile, in the field of image based anomaly detection, Generative Adversarial Networks(GANs) is developed due to its ability to model the complex high-dimensional distribution of images. In this paper, we propose a novel GAN-based anomaly detection and localization framework along with a transformation method for time series imaging, called distance image. Our goal is to learn a mapping a series of distance image to the next distance image. The transforming multivariate time series into 2D image allows us to exploit encoder and decoder structure. Especially, the encoder with pointwise convolution in a series of images ensures to encode temporal information of each time series data as well the correlation between each variable. As a result, an anomaly can be detected and localized by conducting a residual image and an anomaly score function. We empirically demonstrate the effectiveness of our approach for anomaly detection tasks on a real-world power plant data.