Date of Award

6-2017

Degree Name

MS in Computer Science

Department

Computer Science

Advisor

John Bellardo

Abstract

Spacecraft health monitoring is essential to ensure that a spacecraft is operating properly and has no anomalies that could jeopardize its mission. Many of the current methods of monitoring system health are difficult to use as the complexity of spacecraft increase, and are in many cases impractical on CubeSat satellites which have strict size and resource limitations. To overcome these problems, new data-driven techniques such as Inductive Monitoring System (IMS), use data mining and machine learning on archived system telemetry to create models that characterize nominal system behavior. The models that IMS creates are in the form of clusters that capture the relationship between a set of sensors in time series data. Each of these clusters define a nominal operating state of the satellite and the range of sensor values that represent it. These characterizations can then be autonomously compared against real-time telemetry on-board the spacecraft to determine if the spacecraft is operating nominally.

This thesis presents an adaption of IMS to create a spacecraft health monitoring system for CubeSat missions developed by the PolySat lab. This system is integrated into PolySat's flight software and provides real time health monitoring of the spacecraft during its mission. Any anomalies detected are reported and further analysis can be done to determine the cause. The system can also be used for the analysis of archived events. The IMS algorithms used by the system were validated, and ground testing was done to determine the performance, reliability, and accuracy of the system. The system was successful in the detection and identification of known anomalies in archived flight telemetry from the IPEX mission. In addition, real-time monitoring performed on the satellite yielded great results that give us confidence in the use of this system in all future missions.

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