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Authors: M. Alandihallaj 1 ; Mahya Ramezani 2 and Andreas Hein 1

Affiliations: 1 Space Systems Engineering (SpaSys) Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg ; 2 Automation and Robotics Research Group (ARG), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg

Keyword(s): Model-Based Systems Engineering, LSTM, AI, Satellite Failure Prediction, Space System Reliability.

Abstract: 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 decision-making. 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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Alandihallaj, M.; Ramezani, M. and Hein, A. (2024). MBSE-Enhanced LSTM Framework for Satellite System Reliability and Failure Prediction. In Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - MBSE-AI Integration; ISBN 978-989-758-682-8; ISSN 2184-4348, SciTePress, pages 349-356. DOI: 10.5220/0012607600003645

@conference{mbse-ai integration24,
author={M. Alandihallaj. and Mahya Ramezani. and Andreas Hein.},
title={MBSE-Enhanced LSTM Framework for Satellite System Reliability and Failure Prediction},
booktitle={Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - MBSE-AI Integration},
year={2024},
pages={349-356},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012607600003645},
isbn={978-989-758-682-8},
issn={2184-4348},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - MBSE-AI Integration
TI - MBSE-Enhanced LSTM Framework for Satellite System Reliability and Failure Prediction
SN - 978-989-758-682-8
IS - 2184-4348
AU - Alandihallaj, M.
AU - Ramezani, M.
AU - Hein, A.
PY - 2024
SP - 349
EP - 356
DO - 10.5220/0012607600003645
PB - SciTePress

- Science and Technology Publications, Lda.
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