TY - GEN
T1 - Implementation of Machine Learning Techniques upon Model Based Safety Analysis Tools
AU - Abdellatif, Akram
AU - Khattab, Nada
AU - Abdalla, Aya
AU - Holzapfel, Florian
N1 - Publisher Copyright:
Copyright © 2021 by International Astronautical Federation (IAF). All rights reserved.
PY - 2021
Y1 - 2021
N2 - System safety analysis techniques are used mainly during the design of safety critical systems. As these analyses are usually based on an informal system model, they will not be complete, consistent, and error free. Model-Based Safety Analysis (MBSA) is an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. Most MBSA tools are based upon various search and condition algorithms. However, the aspect of a learning curve is always missing. This paper introduces the implementation of machine learning algorithms in order to enhance the extracted failure conditions. The developed algorithm will be applied upon the already developed techniques[1][3]. The algorithm is trained with various resolved flight control systems. The performance of the algorithm is investigated after the addition of the machine learning aspect.
AB - System safety analysis techniques are used mainly during the design of safety critical systems. As these analyses are usually based on an informal system model, they will not be complete, consistent, and error free. Model-Based Safety Analysis (MBSA) is an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. Most MBSA tools are based upon various search and condition algorithms. However, the aspect of a learning curve is always missing. This paper introduces the implementation of machine learning algorithms in order to enhance the extracted failure conditions. The developed algorithm will be applied upon the already developed techniques[1][3]. The algorithm is trained with various resolved flight control systems. The performance of the algorithm is investigated after the addition of the machine learning aspect.
KW - Flight Control Systems
KW - K Nearest Neighbor
KW - Machine Learning
KW - Model-Based Safety Analysis
KW - Reinforcement Learning
KW - Safety Analysis
UR - http://www.scopus.com/inward/record.url?scp=85127402741&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85127402741
T3 - Proceedings of the International Astronautical Congress, IAC
BT - 54th IAA Symposium on Safety, Quality and Knowledge Management in Space Activities 2021 - Held at the 72nd International Astronautical Congress, IAC 2021
PB - International Astronautical Federation, IAF
T2 - 54th IAA Symposium on Safety, Quality and Knowledge Management in Space Activities 2021 at the 72nd International Astronautical Congress, IAC 2021
Y2 - 25 October 2021 through 29 October 2021
ER -