Fractal-based Satellite Health Monitoring

Satellites and space systems are crucial to the success of modern space exploration, particularly as the scale and complexity of missions continue to increase. Given the high investment costs and the impossibility of physical intervention once in orbit, designing reliable and fault-tolerant platforms is crucial for success. Nevertheless, the extreme and unpredictable conditions of the space environment frequently lead to anomalies that threaten mission success.Telemetry data is therefore indispensable for real-time and predictive monitoring of system health. However, its complexity, multidimensionality, and the presence of noise pose significant challenges to traditional analytical techniques. In this context, fractal analysis provides a robust set of tools for uncovering hidden patterns in telemetry signals, enabling the early detection of system degradation and anomalies. Unlike conventional threshold-based approaches, fractal methods are sensitive to changes in signal regularity and complexity, making them suitable for pre-failure diagnostics and trend forecasting. This work investigates the use of fractal-based techniques for satellite health monitoring. The methods are applied to the Mission 1 dataset of the ESA Anomaly Detection Benchmark (ESA-ADB) database, enabling performance evaluation under realistic operational conditions. A comparative analysis is conducted to assess the diagnostic capability, robustness, and computational efficiency of each method, with a focus on identifying subtle anomalies and facilitating proactive decision-making. The results highlight the potential of fractal techniques to enhance the interpretability and autonomy of satellite prognostics and health management (PHM) systems. By enabling more sensitive and timely diagnostics, this approach contributes to improving the operational resilience and life-cycle management of future space missions.