White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Using Safety Critical Artificial Neural Networks in Gas Turbine Aero-Engine Control

Kurd, Z. and Kelly, T.P. (2005) Using Safety Critical Artificial Neural Networks in Gas Turbine Aero-Engine Control. In: 24th International Conference on Computer Safety, Reliability and Security. SAFECOMP 2005, September 28-30, 2005, Fredrikstad, Norway. Lecture Notes in Computer Science (3688). Springer , Berlin / Heidelberg , pp. 136-150. ISBN 978-3-540-29200-5

Full text not available from this repository.


‘Safety Critical Artificial Neural Networks’ (SCANNs) have been previously defined to perform nonlinear function approximation and learning. SCANN exploits safety constraints to ensure identified failure modes are mitigated for highly-dependable roles. It represents both qualitative and quantitative knowledge using fuzzy rules and is described as a ‘hybrid’ neural network. The ‘Safety Lifecycle for Artificial Neural Networks’ (SLANN) has also previously defined the appropriate development and safety analysis tasks for these ‘hybrid’ neural networks. This paper examines the practicalities of using the SCANN and SLANN for Gas Turbine Aero-Engine control. The solution facilitates adaptation to a changing environment such as engine degradation and offers extra cost efficiency over conventional approaches. A walkthrough of the SLANN is presented demonstrating the interrelationship of development and safety processes enabling product-based safety arguments. Results illustrating the benefits and safety of the SCANN in a Gas Turbine Engine Model are provided using the SCANN simulation tool.

Item Type: Proceedings Paper
Institution: The University of York
Academic Units: The University of York > Computer Science (York)
Depositing User: York RAE Import
Date Deposited: 09 Apr 2009 11:07
Last Modified: 09 Apr 2009 11:07
Published Version: http://dx.doi.org/10.1007/11563228_11
Status: Published
Publisher: Springer
Identification Number: 10.1007/11563228_11
URI: http://eprints.whiterose.ac.uk/id/eprint/5505

Actions (repository staff only: login required)