Abstract
Summary — Large Low Earth Orbit satellite
constellations require Machine Learning methods for enabling autonomy in
health keeping of the satellites. Autonomy in health keeping entail’s
fault detection, isolation and reconfiguration. However, prior to
building model building, it becomes imperative to conduct Exploratory
Data Analysis of the data to gain an intuition of data and to decide the
best model. Univariate Exploratory data analysis has been carried out on
a BUS CURRENT sensor of Electrical Power System of a Low Earth Orbit
Satellite to gain an understanding of data. Various aspects of data like
presence of outliers, sampling frequency, missing values, comparison of
imputation methods to fill missing values seasonality and trend
analysis, stationarity test on data, rolling mean, and auto correlation
and partial auto correlation plots have been made and a detailed
statistical analysis of results has been conducted.
