Impact Damage Detection in Aircraft Composites Using Knowledge-based Reasoning

Abstract

A hybrid reasoning methodology is applied to a complex aerospace structure, and its effectiveness is assessed in identifying and locating the position of impacts. Part of a commercial aircraft wing flap is impacted and time-varying strain response data from the structure are sensed using passive piezoceramic sensors. This structure can be regarded as a small scale version of part of a wing span with the corresponding features being a leading edge and trailing edge. The trailing edge is composed of aluminium skins with an aluminium honeycomb core, the leading edge of composite skins with a light weight honeycomb core, and the central section of thin composite material. Nine sensors, to detect time-varying strain response data, are distributed over the surface of the flap; two on the leading edge, two on the trailing edge, and five in the central section. The methodology combines the use of: (i) Case-Based Reasoning; in a `learning mode', an initial casebase is created with the principal features of the impact responses. When the system is working in an `operating mode', the data acquired by sensors are used to perform a diagnosis by analogy with the cases stored in the casebase: reusing and adapting old situations. (ii) The Wavelet Transform is used to extract principal features of a signal providing information about the impact locations. (iii) Self-Organizing Maps are trained as a classification tool in order to organize the old cases in memory with the purpose of speeding up the reasoning process. Finally, when old similar cases are retrieved, the impact location is obtained directly from heuristic considerations.

Keywords

impact damage detection case-based reasoning self-organizing map aircraft structure

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