A sensor selection method using a performance metric for phased missions of aircraft fuel systems

Reeves, Jack; Remenyte-Prescott, R.; Andrews, J.; Thorley, Paul

Abstract

Component failures in complex systems, such as aircraft fuel systems, can have catastrophic effects on system performance. There are a large number of components in these systems, each with a number of different failure modes, some of which can cause system failure. In order to detect and diagnose these component failures, sensors that monitor system performance need to be included. However, the number of sensors installed is typically limited by sensor cost and weight. An approach for selecting sensors could be taken considering sensor usefulness for fault diagnostics.

In this paper, the sensor performance metric proposed by Reeves et al. [1] is extended to consider a phased mission operation, with component failures occurring at various points in the mission. The performance metric favours sensors that can detect the most failures, the failures that affect the system for longest and the failures that cause system failure. In addition, the performance metric considers the ability of sensors to distinguish between component failures, i.e. to diagnose which components have caused the faults observed by these sensors. The proposed approach is illustrated on the Airbus A380-800 fuel system, where the best combination is found using the performance metric within a Genetic Algorithm method.

Citation

Reeves, J., Remenyte-Prescott, R., Andrews, J., & Thorley, P. (2018). A sensor selection method using a performance metric for phased missions of aircraft fuel systems. Reliability Engineering and System Safety, https://doi.org/10.1016/j.ress.2018.07.029

• Journal Article Type: Article
• Acceptance Date: Jul 27, 2018
• Online Publication Date: Jul 27, 2018
• Publication Date: Jul 27, 2018
• Deposit Date: Aug 14, 2018
• Publicly Available Date: Jul 28, 2019
• Journal: Reliability Engineering & System Safety
• Print ISSN: 0951-8320
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.ress.2018.07.029
• Keywords: Sensor selection; Fault diagnostics; Time dependence; Genetic Algorithm; Phased mission
• Public URL: https://nottingham-repository.worktribe.com/output/1030718
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0951832017314631?via%3Dihub
• Contract Date: Aug 14, 2018