Machine Learning Techniques for Satellite Fault Diagnosis

Abstract

Satellites are known as a remotely operated systems with high degree of complexity due to large number of interconnected devices onboard the satellite. Consequently, it has corresponding significant number of telemetry parameters to allow operator and designers have full control and monitor of satellite mode of operation. The tremendous amount of telemetry data received from the satellite, during its lifetime, has to be analyzed in order to monitor and control subsystems health for better decision making and fast responsively. In this research, we address the topic of using machine learning techniques to diagnose faults of satellite subsystems using its telemetry parameters. The case study and source of telemetry are acquired from Egyptsat-1 satellite which has been launched April 2007 and lost communication with ground station last 2010. We applied Machine learning techniques in order to identify operating modes and corresponding telemetry parameters. We used Support Vector Machine for Regression to analyze the satellite performance; then a fault diagnosis approach is applied to determine the most probable reason of this satellite failure. Telemetry data is clustered using k-means clustering algorithm in combination with t-distributed stochastic neighbor embedding (t-SNE) function for dimensionality reduction. We classified data using Logical Analysis of Data (LAD) in order to generate positive patterns for each failure class which is used to determine probability failure cause for each telemetry parameter. These probabilities enable Fault Tree Analysis (FTA) to get the most probable cause that lead to satellite failure.