Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels

CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s expe...

Full description

Bibliographic Details
Main Authors: Adolfo Javier Jara Cespedes, Bramandika Holy Bagas Pangestu, Akitoshi Hanazawa, Mengu Cho
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Applied Sciences
Subjects:
anomaly detection
BIRDS project
CubeSat
machine learning
electrical power system
Online Access:https://www.mdpi.com/2076-3417/12/17/8634

Internet

https://www.mdpi.com/2076-3417/12/17/8634

Similar Items