Image Encoding Based Time Series Data Anomaly Detection for Condition Based Maintenance of Weapon Systems

The radar detection weapon system is a surveillance and reconnaissance system designed to precisely detect long-range targets and determine their position and status. A critical component of the radar detection weapon system is the wave guide, which serves as a path for high-frequency signals. Maintaining appropriate temperature and humidity levels is essential to ensure seamless signal transmission, and this is regulated using an air compressor. However, if compressed air leakage occurs, temperature and humidity may rise, leading to performance degradation and potential component failure, necessitating early fault detection. The sensor data collected form actual operations exhibits high-dimensional and nonlinear characteristics, with abnormal patterns appearing irregularly, making numerical approaches challenging. Furthermore, such methods face limitations in effectively learning overall patterns and dependencies over time. In this paper, we propose an anomaly detection model that combines time-domain feature with image encoding to effectively capture nonlinear signal patterns. Experimental result using real-world operational data demonstrate that the proposed method outperforms existing approaches. The proposed approach is expected to be useful for applying Condition-Based Maintenance(CBM) to various equipment that utilizes time-series data.