LSTM-Based Anomaly Detection for Sensor Data Affected by Electromagnetic Interference

Reliable acquisition and processing of sensor data are crucial for numerous technical systems, particularly in safety-critical applications. However, intentional electromagnetic interference (IEMI) can distort sensor signals, leading to inaccurate measurements or communication failures. This paper investigates anomaly detection in sensor signals affected by IEMI, using data generated from LTspice circuit simulations. The simulations model both undisturbed signals and disturbed signals by injecting double-exponential electromagnetic pulses into a PSI5 sensor communication system. We compare traditional statistical methods with deep learning-based approaches. Two LSTM-based models were implemented: (1) a regression-based approach detecting anomalies through prediction errors and (2) a classification-based approach directly identifying anomalous windows. Both approaches were evaluated against a z-score-based baseline. The results show that the classification-based model achieves the highest anomaly detection performance, with an area under the receiver operating characteristic curve of 0.92 and an average precision of 0.79, significantly outperforming the baseline. Future work will explore hybrid models integrating autoencoders and LSTMs in the latent space to enhance robustness.