Feature extraction and fault detection based on telemetry data for Satellite TX-I

In this paper the telemetry data of Satellite TX-I are analyzed in order to have a better understanding of the satellite operating status, and to lay the foundation for fault detection task. Given the high dimensional data, the locally linear embedding (LLE), a kind of manifold learning schemes, is applied to perform dimensionality reduction and feature extraction. Furthermore the data-driven fault detection can be effectively implemented by means of the statistic indexes T² and SPE. Simulation results presented in the paper demonstrate that not only the data processing, like feature extraction, but the fault detection scheme is effective.