Broadband nonlinear delay and sum (NL-DAS) for baseline-free damage imaging using a sparse sensor network

Abstract

The Delay and Sum (DAS) method is a well-known damage imaging technique in sparse array guided wave imaging. However, the DAS method requires prior knowledge on the stable baseline state, which makes it ineffective under different operational or environmental conditions, e.g. temperature and moisture, and altered experimental settings, e.g. degraded bonding quality of sensors.

This paper proposes a Nonlinear Delay and Sum (NL-DAS) method which does not require prior knowledge about the baseline state, but instead exploits the presence of nonlinear wave/defect interactions. Relatively high-power broadband sweep sine signals are supplied to a sparse sensor array to activate a multitude of nonlinear wave/defect interactions. Application of time–frequency filtering to the response signals allows the isolation of damage-induced broadband nonlinear responses. The isolated broadband nonlinear response signals are subsequently decomposed into a series of narrowband tone burst responses from which a set of damage maps are constructed. To improve the quality of the constructed damage maps, an automated framework is proposed to obtain the frequency-dependent directional group velocities without requiring prior knowledge on material properties. Finally, the resulting set of damage maps is fused into a single broadband NL-DAS damage map.

The proposed broadband NL-DAS approach is demonstrated on a simulation dataset, generated with 3D Finite Element method, which is representative for a cross-ply carbon fiber reinforced polymer (CFRP) containing a delamination defect. The damage imaging performance is studied for different test conditions in terms of (i) signal-to-noise ratio, (ii) number of sensors and (iii) excitation bandwidth. Experimental validation is illustrated on a CFRP plate containing a barely visible impact damage, and on a CFRP A320 component with a disbond at one of the stiffeners.