Machine-Learning-Based Methods for Acoustic Emission Testing: A Review

Abstract

Acoustic emission is a nondestructive control technique as it does not involve any input of energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously emitted by a material under stress due to irreversible phenomena such as damage, microcracking, degradation, and corrosion. It is a dynamic and passive-receptive technique that analyzes the ultrasonic pulses emitted by a crack when it is generated. This technique allows for an early diagnosis of incipient structural damage by capturing the precursor signals of the fracture. Recently, the scientific community is making extensive use of methodologies based on machine learning: the use of machine learning makes a machine capable of receiving a series of data, modifying the algorithms as they receive information on what they are processing. In this way, the machine can learn without being explicitly programmed, and this implies a huge use of data and an efficient algorithm to adapt. This review described the methodologies for the implementation of the acoustic emission (AE) technique in the evaluation of the conditions and in the monitoring of materials and structures. The latest research products were also analyzed in the development of new methodologies based on machine learning for the detection and localization of damage for the characterization of the fracture and the prediction of the failure mode. The work carried out highlighted the strong use of these methods, which confirms the extreme usefulness of these techniques in identifying structural damage in scenarios heavily contaminated by residual noise.