SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Space Systems Engineering (SpaSys) Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg

Context (please use complete words only)

Field

Sort by

1 Papers

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

Topics: AI for MBSE; AI-supported modelling (e.g., bots, recommenders, UI adaptation, etc.); Automation and AI-support of MBSE activities

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

Digital Object Identifier: 10.5220/0012607600003645

Proceedings of MBSE-AI Integration 2024, ISBN: 978-989-758-682-8

Quick 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.