Anomaly detection in sensor data via encoding time series into images

Abstract

Detecting anomalies in multivariate time series data is crucial for maintaining the optimal functionality of control system equipment. While existing research has made significant strides in this area, the increasing complexity of industrial environments poses challenges in accurately capturing the interactions between variables. Therefore, this paper introduces an innovative anomaly detection approach that extends one-dimensional time series into two-dimensions to capture the spatial correlations within the data. Unlike traditional approaches, we utilize the Gramian Angular Field to encode the correlations between different sensors at specific time points into images, enabling precise learning of spatial information across multiple variables. Subsequently, we construct an adversarial generative model to accurately identify anomalies at the pixel level, facilitating precise localization of abnormal points. We evaluate our method using five open-source datasets from various fields. Our method outperforms state-of-the-art anomaly detection techniques across all datasets, showcasing its superior performance. Particularly, our method achieves a 11.5% increase in F1 score on the high-dimensional WADI dataset compared to the baseline method. Additionally, we conduct thorough effectiveness analysis, parameter impact experiments, significant statistical analysis, and burden analysis, confirming the efficacy of our approach in capturing both the temporal dynamics and spatial relationships inherent in multivariate time series data.