Date of Award
Fall 12-14-2023
Access Type
Thesis - Open Access
Degree Name
Master of Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Hever Moncayo
Committee Advisor
Hever Moncayo
First Committee Member
Kadriye Merve Dogan
Second Committee Member
Richard Prazenica
College Dean
James W. Gregory
Abstract
This thesis presents recent findings regarding the performance of an intelligent architecture designed for spacecraft fault estimation. The approach incorporates a collection of systematically organized autoencoders within a Bayesian framework, enabling early detection and classification of various spacecraft faults such as reaction-wheel damage, sensor faults, and power system degradation.
To assess the effectiveness of this architecture, a range of performance metrics is employed. Through extensive numerical simulations and in-lab experimental testing utilizing a dedicated spacecraft testbed, the capabilities and accuracy of the proposed intelligent architecture are analyzed. These evaluations provide valuable insights into the architecture's ability to detect and classify different types of faults in a spacecraft system.
The study has successfully implemented an intelligent architecture for detecting and classifying faults in spacecraft. The architecture was analyzed through numerical simulations and experimental tests, demonstrating enhanced early detection capabilities. The incorporation of autoencoders and Bayesian methods proved to be a powerful combination, allowing the architecture to effectively capture and learn from complex spacecraft system dynamics and detect various types of faults.
This research presents an advanced and reliable approach to early fault detection and classification in spacecraft systems, highlighting the potential of the intelligent architecture and paving the way for future developments in the field.
Scholarly Commons Citation
Jado Puente, Rocio, "Deep-Learning Based Multiple-Model Bayesian Architecture for Spacecraft Fault Estimation" (2023). Doctoral Dissertations and Master's Theses. 778.
https://commons.erau.edu/edt/778