Active source localization in wave guides based on machine learning.

In the present work, an active source localization strategy is proposed. The presence of active sources in a waveguide can have several reasons, such as crack initiation or internal friction. In this study, the active source is represented by an impact event. A steel ball is dropped on an aluminum plate at different positions. Elastic waves are excited and will propagate through the plate. The wave response is acquired by a piezoelectric sensor network, which is attached to the plate. After performing numerical and physical experiments, enough data are collected in order to train an artificial neural network and a support vector machine. Those machine learning algorithms will predict the impact position based on the wave response of each sensor, while only numerical data from the finite element simulations are used to train both methods. After the training process is completed, the algorithms are applied to experimental data. A good agreement between reference and predicted results proves that the wave responses at the piezoelectric transducers contain sufficient information in order to localize the impact position precisely.

A novel multi-segment path analysis based on a heterogeneous velocity model for the localization of acoustic emission sources in complex propagation media.

In acoustic emission analysis, common source location algorithms assume, independently of the nature of the propagation medium, a straight (shortest) wave path between the source and the sensors. For heterogeneous media such as concrete, the wave travels in complex paths due to the interaction with the dissimilar material contents and with the possible geometrical and material irregularities present in these media. For instance, cracks and large air voids present in concrete influence significantly the way the wave travels, by causing wave path deviations. Neglecting these deviations by assuming straight paths can introduce significant errors to the source location results. In this paper, a novel source localization method called FastWay is proposed. It accounts, contrary to most available shortest path-based methods, for the different effects of material discontinuities (cracks and voids). FastWay, based on a heterogeneous velocity model, uses the fastest rather than the shortest travel paths between the source and each sensor. The method was evaluated both numerically and experimentally and the results from both evaluation tests show that, in general, FastWay was able to locate sources of acoustic emissions more accurately and reliably than the traditional source localization methods.

Time reverse modeling of acoustic emissions in a reinforced concrete beam.

The time reverse modeling (TRM) is applied for signal-based acoustic emission (AE) analysis of reinforced concrete (RC) specimens. TRM uses signals obtained from physical experiments as input. The signals are re-emitted numerically into a structure in a time-reversed manner, where the wavefronts interfere and appear as dominant concentrations of energy at the origin of the AE. The experimental and numerical results presented for selected AE signals confirm that TRM is capable of localizing AE activity in RC caused by concrete cracking. The accuracy of the TRM results is corroborated by three-dimensional crack distributions obtained from X-ray computed tomography images.