Subject Area

Computer Science

Abstract

Telemetry sensors play a crucial role in spacecraft operations, providing essential data on efficiency, sustainability, and safety. However, identifying irregularities in telemetry data can be a time-consuming process that risks the success of missions. With the rise of CubeSats and smallsats, telemetry data has become more abundant, but concerns about privacy and scalability have resulted in untapped data potential. To address these issues, we propose a new approach to anomaly detection that utilizes machine learning models at data sources. These models solely transmit weights to a centralized server for aggregation, resulting in improved dataset performance with a single global model. We have also incorporated self-attention into the federated process to further enhance anomaly detection performance. Our experiments with real-world telemetry data have demonstrated that our approach is state-of-the-art in that we can construct a single model to address multiple telemetry channels while still adhering to the constraints typically seen in space missions. Our framework streamlines anomaly detection, promoting operational efficiency, sustainability, and safety. It facilitates collaborative insights while abiding by mission security constraints and reducing the risk of accidents and downtime, ensuring sustainability.

Degree Date

Spring 5-11-2024

Document Type

Dissertation

Degree Name

D.Eng.

Department

Computer Science

Advisor

Frank Coyle

Second Advisor

Jia Zhang

Third Advisor

Theodore Manikas

Fourth Advisor

Eric Larson

Fifth Advisor

Jennifer Dworak

Sixth Advisor

Sukumaran Nair

Number of Pages

81

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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