Location of acoustic emission sources generated by air flow

Kosel, Tadej; Grabec, Igor; Mužič, Peter

doi:10.1016/s0041-624x(99)00079-7

Cited by 38 publications

(19 citation statements)

References 3 publications

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“…Therefore, if the propagation distances from the impact point to different receivers vary significantly then, due to dispersion and attenuation, the shapes of the signal recorded by different receivers look significantly different and recording the arrival time by the threshold technique may not give accurate results. In that situation time-domain cross-correlation technique [18,19] applied to two time-domain signals recorded by two sensors can be followed to accurately obtain the difference between the arrival times at the two sensors. Alternately the difference in time-frequency wavelet scalograms [20] can be used for accurately measuring the arrival time differences.…”

Section: Experimental Investigationmentioning

confidence: 99%

Locating point of impact in anisotropic fiber reinforced composite plates

et al. 2008

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Section: Experimental Investigationmentioning

confidence: 99%

Locating point of impact in anisotropic fiber reinforced composite plates

et al. 2008

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“…As presented in reference [8], the impact localization usually can be implemented by three strategies: i) build the relation between the sensor responses and the different impacts by numerical calculation or experiment, and use the inverse methods to estimate the impact [10][11][12]; ii) measure the directivity of the Lamb wave by placing the anisotropic sensors and calculate the location of the impact directly [13][14][15]; iii) get the knowledge of the time difference in wave flight and solve the hyperbolic equations to locate the impact. Because the hyperbolic localization method requires only the group velocity of the Lamb wave and exhibits an effective performance, it has been received more and more interests in passive sensing SHM [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Impact identification using a passive imaging method

Zhang

Qiu

et al. 2014

International Journal of Applied Electromagnetics and Mechanics

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This study presents a novel imaging method in passive sensing structural health monitoring (SHM), for locating impact damage in real-time on a plate-like structure. So far, many imaging methods have already been applied in active sensing SHM to locate the damages. However, seldom of the imaging methods can be directly used to locate the impact in passive sensing SHM. In order to avoid solving the hyperstatic hyperbolic equations and generate an informative image for locating the impact damage, the proposed method combines the imaging method with probability calculation. Moreover, to demonstrate the feasibility of the proposed method, the experimental results are presented and analyzed. From the results, the proposed method shows an interesting phenomenon that the accuracy of the impact localization depends on the impact position. Finally, a detailed analysis of the method accuracy is discussed.

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“…However, the traditional neural network approaches have limitations on local minima and generalisation giving rise to models that can overfit the data [8]. Support vector machine (SVM), which is based on the statistical learning theory, has effectively been developed and applied successfully in machine learning because of the high accuracy and good generalisation [9].…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition Method for the Source Location of Acoustic Emission Generated During the Damage of Hydraulic Concrete

Jianjie³

et al. 2012

Strain

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To overcome the disadvantages of current acoustic emission (AE) source location methods, such as classical approaches based on times of arrival and artificial neural networks based on AE signal features, support vector machines (SVM)-based models have been employed to recognise AE source regions in structures. However, in some circumstances, it seems that a more accurate positioning of AE sources is needed. This study concerns the spatial three-dimensional (3D) positioning (i.e. coordinates) for damages in hydraulic concrete structures using the least squares SVM (LS-SVM) regression with AE signal features. The data of artificial discrete AE sources were acquired from simulated AE events on a hydraulic concrete specimen. Various combinations of signal features were chosen to adequately excavate effective information and to obtain the multi-output LS-SVM regression model of the best performance. The training and testing results show that the proposed model can realise the accurate spatial 3D positioning of damages in hydraulic concrete structures in laboratory situations and reduce human factors (e.g. judgment of AE propagation velocity, etc.) in the AE source location process. Meanwhile, the work remaining in taking this idea to a practical implementation was discussed.

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Location of acoustic emission sources generated by air flow

Cited by 38 publications

References 3 publications

Locating point of impact in anisotropic fiber reinforced composite plates

Locating point of impact in anisotropic fiber reinforced composite plates

Impact identification using a passive imaging method

Pattern Recognition Method for the Source Location of Acoustic Emission Generated During the Damage of Hydraulic Concrete

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