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Project Outline

Project Overview

Technological Objectives

The major hurdle in the development of monitoring techniques specifically for complex structures is the coverage which the technique can provide. Monitoring of prototype aircraft structures in flight with strain gauges or similar sensors is common practice. However, such techniques only provide data relevant to the exact location of the sensor (strain gauge). To get meaningful information covering the bulk of the structure calls for the installation of numerous distributed sensors with their associated cabling and data capture devices. Whereas this approach is feasible for a prototype, the added weight and volume of this equipment would greatly impair the economic use of an operational production aircraft. Furthermore, these systems still only monitor an array of isolated points and information about the whole structure can only be obtained by extrapolation between the data obtained by nearest- neighbour sensors. By developing new and novel structural monitoring techniques which overcome these limitations by monitoring all points in an air-fame with a limited number of sensors, the consortium SMEs will access the €205 million inspection budget of European aircraft manufacturers and operators 4 years after project completion.

Project Goals

The project will develop an advanced integrated system for real-time, in-flight SHM and impending failure detection for aircraft components. This will enable a fundamental realignment of inspection/maintenance strategies, which can then be based on the actual momentary condition of the aircraft structure. The capability of the developed system will be demonstrated in laboratory tests on large scale aircraft components.

Guided wave technology is a promising technique for structural monitoring in that it provides large area coverage from a limited number of sensors, combined with potentially high defect detection sensitivity. However, due to the effects of dispersion of the ultrasound and to the complex non-constant geometry of the object under inspection and to the changing conditions (loading, temperature etc.), the interpretation of signals can be very complicated.

The change of a signal with time can be detected at much lower signal to noise ratios than 'absolute' signal amplitude especially when deploying a neural network in the signal processing routine. The enhanced sensitivity of our proposed monitoring technique rests in this fact.