Satellite battery sensor values prediction using Bayesian ridge regression models
Galal, M., Hussein, W., Abdelkawy, E. (2019). Satellite battery sensor values prediction using Bayesian ridge regression models. EKB Journal Management System, 18(18), 1-6. doi: 10.1088/1757-899X/610/1/012012
Mohamed A Galal; Wessam M Hussein; Ezz El-din Abdelkawy. "Satellite battery sensor values prediction using Bayesian ridge regression models". EKB Journal Management System, 18, 18, 2019, 1-6. doi: 10.1088/1757-899X/610/1/012012
Galal, M., Hussein, W., Abdelkawy, E. (2019). 'Satellite battery sensor values prediction using Bayesian ridge regression models', EKB Journal Management System, 18(18), pp. 1-6. doi: 10.1088/1757-899X/610/1/012012
Galal, M., Hussein, W., Abdelkawy, E. Satellite battery sensor values prediction using Bayesian ridge regression models. EKB Journal Management System, 2019; 18(18): 1-6. doi: 10.1088/1757-899X/610/1/012012

Satellite battery sensor values prediction using Bayesian ridge regression models

International Conference on Aerospace Sciences and Aviation Technology
Article 14, Volume 18, Issue 18, April 2019, Page 1-6  XML PDF (579.04 K)
Document Type: Original Article
DOI: 10.1088/1757-899X/610/1/012012
View on SCiNiTO View on SCiNiTO
Authors
Mohamed A Galal1; Wessam M Hussein2; Ezz El-din Abdelkawy1
1Department of Electrical Engineering, Military Technical College, 11766, Cairo, Egypt.
2Department of Mechatronic Engineering, Military Technical College, 11766, Cairo, Egypt.
Abstract
Proper mission control plays a key role in the lifetime of space mission operation, as it ensures that all resources are efficiently utilized when achieving mission goals. Ground control station operation mainly depends on received telemetry together with models simulating spacecraft`s subsystems. Created models help in raising the level of autonomy of MCC (Mission Control Center). Data driven models describe the actual state of the subsystem in real operation situations rather than theoretical costly physical models. This paper proposes data driven models for satellite battery subsystem based on Bayesian ridge regression algorithm. The ridge coefficients minimize a penalized residual sum of squares Thirty models of all thirty battery variables (capacitance, voltage, pressure and temperature) are built from normal operation data. Sensor reading value can be predicted from an observation of all other 29 values. Faults present in sensors or in system can be detected if predicted values are not equal to actual downloaded data from satellite. Bayesian ridge regression models are validated in terms of slope, intercept, R2-value, Q2 -value P-value and standard error.
Statistics
Article View: 66
PDF Download: 75