Abstract
he acoustic emission technique has been applied successfully for the identification, characterization, and localization of deformations in civil engineering structures. Numerous localization techniques, such as Modal Acoustic Emission, Neural Networks, Beamforming, and Triangulation methods with or without prior knowledge of wave velocity, have been presented. Several authors have conducted in-depth research in the localization of AE sources. However, existing review papers do not focus on the performance evaluation of existing source localization techniques. This paper discusses these techniques based on the number of sensors used and the geometry of the structures of interest. Furthermore, it evaluates them on the basis of their performance. At the end of this paper, a comparative analysis of existing methods has been presented based on their basic principles, key strengths, and limitations. A deep learning circular sensor cluster-based solution has the potential to provide a low-cost reliable localization solution for acoustic emission sources.
Highlights
The localization of active damages using a set of sensors has become an interesting topic in recent years
Analytical techniques for acoustic emissions (AE) source localization are challenging to apply in real-life structures that are complex
Soft computing techniques like artificial neural networks (ANN) have a tremendous potential for acoustic emission source localization
Summary
The localization of active damages using a set of sensors has become an interesting topic in recent years. It has been used extensively in many fields, such as structural health monitoring, deep mining, and intrusion detection. A variety of non-destructive testing (NDT) methods such as smart pigs, GPS mapping devices, guided wave ultrasonic, hydrostatic, and acoustic emissions (AE) have been used to monitor structures. Among these methods, AE is a passive method that can detect any deformation in various material structures.
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