Data Driven Models for Satellite State-of-Health Monitoring and Evaluation

Abstract

Satellite state-of-health conditions and keeping up systems safe are the fundamental worry of space operations. Since managing the correlation structure between variables give greater information than individual variables or causes relationships, Data driven methodology presented as an answer of managing enormous information. In this article a survey of some recent related work is done. Then a mathematical foundation for latent variable modeling, support vector machines (SVMs) and artificial neural networks (ANN) is illustrated. Then a four data driven models are introduced for monitoring, diagnosis and prediction of the thermal control of satellite power supply system. Two kinds of PLS-Batch models were implemented for the first time with a satellite telemetry. After that a utilization of multivariate Shewart, DModX in addition to CuSum charts for system monitoring and early fault detection. Another two machine learning models were built and coded in Python. These two models are (SVMs) model and (ANN) model. Both of the last two models are integrated with PCA for feature extraction and dimensionality reduction for visualization. The outcomes confirmed the effectiveness of utilizing such models in monitoring, faults detection, and diagnosis. Also system health evaluation could be established within the operation through these models. In addition to prediction of system behavior and early faults detection.