Deep-Neural-Network-based anomaly detector for DC/DC power converter failure detection
Subject:
DC/DC Converters
Power electronics
Space
Artificial Intelligence
Publication date:
Abstract:
The Electrical Power Subsystem (EPS) of a spacecraft is paramount to its operation since it will guarantee that every piece of equipment is receiving its required power. Therefore, the reliability of the power subsystem is one of the cornerstones of the full spacecraft reliability. DC/DC converters are one of the main constituents of the power subsystems. A method able to estimate the degradation of a dc-dc converter would enhance the power system reliability. It would allow to detect dc-dc converters prone to failure and to take corrective actions to extend their remaining lifespan.
The Electrical Power Subsystem (EPS) of a spacecraft is paramount to its operation since it will guarantee that every piece of equipment is receiving its required power. Therefore, the reliability of the power subsystem is one of the cornerstones of the full spacecraft reliability. DC/DC converters are one of the main constituents of the power subsystems. A method able to estimate the degradation of a dc-dc converter would enhance the power system reliability. It would allow to detect dc-dc converters prone to failure and to take corrective actions to extend their remaining lifespan.
Description:
SPAICE Conference on AI in and for Space (1st. 2024. European Centre for Space Applications and Telecommunications (ECSAT), UK)
Patrocinado por:
This work has been funded by the Spanish Ministry of Science through PID2021-127707OB-C21 and by the Principality of Asturias through PA-23-BP21-207.