An Empirical Evaluation of Machine Learning for Anomaly Detection in Electromagnetic Compatibility

Real-time fault detection in electromagnetic compatibility is critical for dependable electronic system operation. This paper evaluates anomaly detection algorithms applied to time-domain electromagnetic compatibility measurements, meanwhile also addressing the lack of standardized datasets in this area. A novel publicly available dataset was generated using a custom test bench, injecting controlled disturbances to simulate real-world anomalies of varying severity and frequency. In terms of representation, the raw three-phase current signals were compared to a lower-dimensional representation obtained by leveraging Clarke-Park transformations. This study systematically compares a variety of classical statistical methods and modern machine learning techniques. The performance of these methods was assessed using five key anomaly detection performance metrics, providing insights into the models’ effectiveness. The results demonstrate the importance of appropriate preprocessing and its relationship to key characteristics of the anomaly detection methods.