Radar Health Monitoring Using Anomaly Detection

Abstract

Predictive maintenance has emerged as a crucial strategy in complex systems management, leveraging machine learning and data-driven health monitoring to anticipate failures and optimize operational uptime. While significant progress has been made in developing general-purpose models for anomaly detection and condition-based maintenance, their effectiveness often diminishes when applied to highly specialized systems such as radar platforms. These systems exhibit unique operational behaviours and failure modes, necessitating tailored monitoring solutions. This paper presents a methodology for anomaly detection tailored to radar systems, addressing the inherent challenge of limited labeled data and the ambiguity surrounding the definition of anomalies. We employ a reconstruction-based approach using autoencoders in conjunction with Mahalanobis distance to generate anomaly scores, enabling the detection of subtle deviations from normal system behavior without requiring explicit failure labels. The proposed approach has been applied to real sensor data collected from multiple radar units, specifically from sensors located on the antenna mast. For confidentiality, the data has been anonymized. Experimental results demonstrate that the method effectively highlights outliers and identifies the contributing features responsible for anomalies. Furthermore, the model reveals interpretable abnormal patterns and provides early indications that condition-based monitoring can be a viable strategy for identifying potential issues in radar operations.