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Detecting impacts on a representative aerospace structure: an implementation with tests

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conference contribution
posted on 02.10.2015, 12:56 authored by Sam Bemment, Ian Read, Peter HubbardPeter Hubbard
Collisions with aerial objects, e.g. bird strikes, pose a threat to aircraft in flight. In conventional aircraft, the pilot(s) would typically be aware of any significant collision through control response, noise, or visual indicators, and can fulfil the regulatory requirement of reporting the incident. In a UAV (Unmanned Aerial Vehicle), there is a requirement to automate these functions. The aim of the work detailed in this paper is to demonstrate that acoustic emission sensing equipment developed in the laboratory, and described in previous literature, can also be used to detect impacts on a large scale aerospace structure. The test structure for this work is a BAE Systems HERTI (High Endurance Rapid Technology Insertion) UAV. Simulated bird strike impacts are performed along the leading edge. Measurements are transmitted from sensors mounted on the wing to a processing system that deduces the location and energy of the impact by comparing the range of acoustic signatures. It is shown that the use of an array of 3 sensors enables repeatable detection and location of low energy impacts, demonstrating that acoustic detection of impacts is possible on a representative aerospace structure.



  • Mechanical, Electrical and Manufacturing Engineering

Published in

International Workshop on Structural Health Monitoring


BEMMENT, S.D., READ, I. and HUBBARD, P.D., 2015. Detecting impacts on a representative aerospace structure: an implementation with tests. In: Proceedings of the International Workshop on Structural Health Monitoring, Stanford University, 1-3 September 2015


© DEStech Publications, Inc.


AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

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This is a conference paper.




Stanford University

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