Data Driven Framework for Anomaly Detection and Inter Subsystem Correlation in CubeSat Telemetry

Abstract

CubeSats have become an important platform for low-cost space missions by integrating multiple tightly coupled subsystems whose interactions strongly affect mission performance and reliability. These nanosatellites continuously generate massive volumes of telemetry data that are used to monitor subsystems health. Most existing studies analyze telemetry data at the level of individual subsystems, which limits the understanding of system-level interactions and dependencies. This paper proposes a data-driven framework to investigate inter-subsystem relationships in CubeSat platforms using telemetry data. The approach first applies a threshold-based Out-Of-Limits anomaly detection method to identify abnormal operational periods. Temporal windowing is then performed around detected anomalies, followed by multi-subsystem data fusion. Cross-subsystem dependencies are quantified using the Pearson correlation coefficient, enabling the analysis of statistical relationships between telemetry parameters from different subsystems. The framework is evaluated using telemetry data from five CubeSat subsystems: Electrical Power System, Attitude Determination and Control System, Communications, Flight Computer, and Payload Controller. The results show that meaningful correlation patterns emerge during abnormal operating conditions, particularly between the electrical Power System and the attitude Determination and Control System. The temporal behavior of these correlations highlights dynamic interaction mechanisms within the CubeSat architecture. The proposed framework offers a lightweight and interpretable approach for system-level telemetry analysis and provides a structured basis for building well-prepared datasets for future anomaly detection models without relying on detailed physical models or labeled fault data.