State-of-the-Art Review on the Acoustic Emission Source Localization Techniques (original) (raw)
Related papers
Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning
Aerospace
This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu-Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources.
Fast and Reliable Acoustic Emission Source Location Technique in Complex Structures
Acoustic emission (AE) provides engineers with a powerful tool by allowing the location of damage sources as they occur. Damage localisation using traditional time of arrival approaches is inadequate in complex structure components. Cardiff University presented a novel approach known as Delta-T mapping which overcame these limitations but it was considered as time consuming and an operator dependent approach. This paper presents new full automatic Delta-T mapping technique overcomes these remaining limitations.
Ultrasonics, 2020
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Acoustic emission localization on ship hull structures using a deep learning approach
2016
In this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor.
Intelligent location of simultaneously active acoustic emission sources: Part I
Aircraft engineering, 2003
The intelligent acoustic emission locator is described in Part I, while Part II discusses blind source separation, time delay estimation and location of two simultaneously active continuous acoustic emission sources. The location of acoustic emission on complicated aircraft frame structures is a difficult problem of non-destructive testing. This article describes an intelligent acoustic emission source locator. The intelligent locator comprises a sensor antenna and a general regression neural network, which solves the location problem based on learning from examples. Locator performance was tested on different test specimens. Tests have shown that the accuracy of location depends on sound velocity and attenuation in the specimen, the dimensions of the tested area, and the properties of stored data. The location accuracy achieved by the intelligent locator is comparable to that obtained by the conventional triangulation method, while the applicability of the intelligent locator is more general since analysis of sonic ray paths is avoided. This is a promising method for non-destructive testing of aircraft frame structures by the acoustic emission method.
A Machine Learning Approach for Locating Acoustic Emission
EURASIP Journal on Advances in Signal Processing, 2010
This paper reports on the feasibility of locating microcracks using multiple-sensor measurements of the acoustic emissions (AEs) generated by crack inception and propagation. Microcrack localization has obvious application in non-destructive structural health monitoring. Experimental data was obtained by inducing the cracks in rock specimens during a surface instability test, which simulates failure near a free surface such as a tunnel wall. Results are presented on the pair-wise event correlation of the AE waveforms, and these characteristics are used for hierarchical clustering of AEs. By averaging the AE events within each cluster, "super" AEs with higher signal to noise ratio (SNR) are obtained and used in the second step of the analysis for calculating the time of arrival information for localization. Several feature extraction methods, including wavelet packets, autoregressive (AR) parameters, and discrete Fourier transform coefficients, were employed and compared to identify crucial patterns related to Pwaves in time and frequency domains. By using the extracted features, an SVM classifier fused with probabilistic output is used to recognize the P-wave arrivals in the presence of noise. Results show that the approach has the capability of identifying the location of AE in noisy environments.
Proceedings of the 9th International Conference on Fracture Mechanics of Concrete and Concrete Structures, 2016
FastWay is a novel method for source localization of acoustic emissions (AE) in complex solid media. It uses the fastest, rather than the shortest wave path between the AE source and the recording sensors. In this paper we investigate the potential of this method for acoustic emission source localization in concrete structures. To consider the influence on the wave propagation of both the concrete heterogeneity and the possible cracks present in the tested specimen, a heterogeneous velocity model was selected and a multi-segment path analysis based on this model was performed. After validating the model numerically using simulated AE sources, laboratory experiments were conducted on a small concrete beam (152 mm × 152 mm × 533 mm) with a predefined notch cut to serve as a material discontinuity (crack). Artificial AE sources using pencil-lead breaks were applied on a 25.4 mm × 25.4 point grid mapped on the surface of the beam. To evaluate the performance of FastWay, a set of sources randomly selected were picked and localization results using both FastWay and Geiger's method compared. The results obtained show that FastWay performs more reliably and accurately than Geiger's algorithm even in the presence of cracks and air inclusions. No major influence of these two factors was seen on the localization results. The influence considered the most crucial, however, is of the velocity model which strongly depends on the complex internal structure of the tested specimen.
A generic technique for acoustic emission source location
Acoustic emission (AE) source location is an essential part of any quantitative AE test as it provides information about damage mechanisms and allows spatial separation so that signals from unwanted sources can be eliminated. In this paper, an AE source location technique described as the best-matched point search method is presented. The application of the bestmatched point search method is demonstrated in two source location experiments: one on a large anisotropic carbon-fibre composite (CFC) plate and one on a thick oolitic limestone disc. In the large composite plate test, source location is achieved using the S 0 mode, which displays a complicated group velocity pattern. In the oolitic limestone experiment, three-dimensional source location is demonstrated. The best-matched point search method successfully determines the location of AE sources in both tests. Errors in source location are attributed to the extraction of delta-t times from the AE signals.
Intelligent location of two simultaneously active acoustic emission sources
Aerospace Science and Technology, 2005
The intelligent acoustic emission locator is described in Part I, while Part II discusses blind source separation, time delay estimation and location of two simultaneously active continuous acoustic emission sources. The location of acoustic emission on complicated aircraft frame structures is a difficult problem of non-destructive testing. This article describes an intelligent acoustic emission source locator. The intelligent locator comprises a sensor antenna and a general regression neural network, which solves the location problem based on learning from examples. Locator performance was tested on different test specimens. Tests have shown that the accuracy of location depends on sound velocity and attenuation in the specimen, the dimensions of the tested area, and the properties of stored data. The location accuracy achieved by the intelligent locator is comparable to that obtained by the conventional triangulation method, while the applicability of the intelligent locator is more general since analysis of sonic ray paths is avoided. This is a promising method for non-destructive testing of aircraft frame structures by the acoustic emission method.