A Comparative Analysis of Anomaly Detection Techniques for Battery Telemetry Data in Low Earth Orbit Remote Sensing Satellites

Abstract

The article presents a comprehensive assessment of telemetry data of batteries used in low-earth orbit satellites. The study further performs an analysis of the performance of using different anomaly detection techniques, including Statistical (Z-Score), Machine Learning (One class support vector machine OCSVM, Isolation Forest), Deep Learning (Autoencoder), and Hybrid Approaches (Autoencoder and neural network and Autoencoder and Z-score). This study introduces and evaluates a hybrid anomaly detection framework combining deep learning-based feature compression (Autoencoder) with various downstream classifiers. The models are validated on real satellite telemetry data and benchmarked using medical electrocardiogram ECG datasets for generalizability. In addition, the study continues to analyze the system by detecting the faulty sensor that was responsible for the detected anomalies, which can help the operators to get a more accurate analysis of the system.