Structural health monitoring (SHM) techniques are widely used in industry applications to guarantee the integrity of several types of components. Ultrasonic guided wave (GW) methods for damage imaging typically use… Click to show full abstract
Structural health monitoring (SHM) techniques are widely used in industry applications to guarantee the integrity of several types of components. Ultrasonic guided wave (GW) methods for damage imaging typically use baseline signals from the undamaged component, which are often affected by real operational conditions and may not always be available. This article proposes a baseline-free damage imaging algorithm based on spatial frequency domain virtual time reversal (SFD-VTR). Virtual time reversal (VTR) is an alternative to traditional time reversal as it reduces the burden of physically back-propagating re-emitted signals by applying signal operations between the transmitted and received waveforms. However, VTR relies on the reconstruction of the emitted signal in the time domain, which involves significant data manipulation causing sampling errors of reconstructed signals. These errors are largely manifested in nonstationary transient phenomena, such as GW propagation and may lead to poor damage detection. Novel SFD-VTR damage indices were here proposed to enhance defect detection as they do not require the time domain reconstruction of re-emitted signals. Therefore, this work develops a new baseline-free algorithm for damage detection based on a mathematical model of acoustic emission wave propagation for a network of emitter-receivers. The algorithm was validated in aluminum and composite specimens, and it was able to localize material flaws with a maximum localization error of ∼6 mm and ∼2 mm for the aluminum and composite samples, respectively, proving to be a promising alternative to SHM systems. Besides, this new mathematical model optimized the traditional methodologies by excluding signal emissions from the setup, and digital signal processing steps.
               
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