Towards Explainable Anomaly Detection for Satellite Telemetry

Abstract

The increasing complexity of modern satellites and the growing amount of telemetry data available pose significant challenges for a safe and economic operation of satellites. To support the satellite engineers, traditional machine learning methods, including deep learning-based approaches, have shown promising results but lack intuitive explainability, hindering their adoption in operational settings. This paper presents a novel approach to anomaly detection and causal inference in satellite telemetry data, leveraging an ensemble of classical statistical models and deep learning architectures, combined with causal discovery techniques. We investigate the Peter and Clark Momentary Conditional Independence algorithm for identifying causal relationships with temporal dependencies and compare its results with root cause analysis from the anomaly detection. Our approach identifies 16 potential anomalies and provides counterfactual explanations to facilitate interpretation by satellite operators. By integrating causal inference methods into anomaly detection pipelines, we aim to enhance explainability and facilitate decision-making in complex systems. This paper contributes to the growing body of work on anomaly detection and causal inference, highlighting the potential of combining machine learning and causal inference for improved operational performance.