MBSE-Enhanced LSTM Framework for Satellite System Reliability and Failure Prediction

This paper investigates the integration of Artificial Intelligence (AI) and Model-Based Systems Engineering (MBSE) in the field of satellite system reliability. We employ Long Short-Term Memory (LSTM) networks, an AI technique, to predict the failure probabilities of various subsystems. These LSTM models are integrated into an MBSE framework, enhancing the accuracy of system-wide failure prediction and operational decisionmaking. The approach involves training LSTM networks on simulated datasets representing a range of operational scenarios for each subsystem. The outputs from these networks are then aggregated using a weighted approach to determine the optimal disposal time, aiming to extend the satellite's operational lifespan. The performance of the system is evaluated a simulated real mission scenario. This research highlights the potential of AI-MBSE integration in advancing satellite system design and maintenance strategies.