Acoustic emission for monitoring aircraft structures

Holford, Karen Margaret ORCID: https://orcid.org/0000-0002-3239-4660, Pullin, Rhys ORCID: https://orcid.org/0000-0002-2853-6099, Evans, Samuel Lewin ORCID: https://orcid.org/0000-0003-3664-2569, Eaton, Mark Jonathan ORCID: https://orcid.org/0000-0002-7388-6522, Hensman, J. and Worden, K. 2009. Acoustic emission for monitoring aircraft structures. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 223 (5) , pp. 525-532. 10.1243/09544100JAERO404

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Abstract

Structural health monitoring (SHM) is of paramount importance in the aircraft industry: not only to ensure the safety and reliability of aircraft in flight and to ensure timely maintenance of critical components, but also increasingly to monitor structures under test for airworthiness certification of new designs. This article highlights some of the recent advances in the acoustic emission (AE) technique as applied to SHM, and the new approaches that are crucial for the successful use of AE data for diagnostic purposes. These include modal analysis, enhanced location techniques, and novel signal processing approaches. A case study is presented on a landing gear component undergoing fatigue loading in which a linear location analysis using conventional techniques identified the position of fracture and final rupture of the specimen. A principal component analysis approach was used to separate noise signals from signals arising from fatigue cracks, which identified and located further fatigue crack positions, subsequently confirmed by magnetic particle inspection. Kernel probability density functions are used to aid visualization of the damage location.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	acoustic emission; aircraft components; fault detection; fatigue cracks; fault location; signal processing; principal component analysis
Publisher:	Sage
ISSN:	0954-4100
Last Modified:	02 Dec 2023 14:36
URI:	https://orca.cardiff.ac.uk/id/eprint/5429

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