Acoustic Emission Source Localization in Plate-Like Structure

Dris, El yamine; Drai, Redouane; Benammar, Abdessalem; Berkani, Daoud

doi:10.1109/eecs.2017.44

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Selecting a specific frequency enables the determination of the corresponding velocity. Continuous wavelet transformation has proven to be a more robust method in source localization in plates 10 . WT provides a more precise and efficient tool for signal-processing tasks.…”

Section: Damage Detection In Dispersive Mediamentioning

confidence: 99%

“…COMSOL numerical model of (a) a unit cell model of an aluminum plate and (b) the dispersion curve 3.2 Wavelet transform The wavelet transformation (WT) technique decomposes transient signals into their harmonic components. It offers a solution to the limitations faced by traditional fast Fourier transformation, especially dealing with signals that exhibit discontinuities.Continuous wavelet transformation has proven to be a more robust method in source localization in plates10 . WT provides a more precise and efficient tool for signal-processing tasks.…”

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confidence: 99%

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Multi-frequency MEMS sensor for single-point source localization in dispersive media using modal acoustic emission and wavelet transformation

Taha,

Zhang,

Khan

et al. 2024

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024

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The conventional method for one-dimensional acoustic emission (AE) source localization relies on two sensors to calculate the time-of-flight (TOF) and subsequently determine the source location given the wave speed. However, this method encounters inaccuracies when applied to dispersive mediums. To address this challenge, we suggest a novel method for single-sensor source localization. This method leverages multi-frequency Micro-Electro-Mechanical Systems (MEMS) equipped with sixteen resonators, each tuned to a specific frequency within the 100 kHz to 700 kHz range. By employing the wavelet transformation technique, we can determine the arrival times of these sixteen unique frequencies. By incorporating these arrival times with the dispersion curve, obtained from numerical simulation, it becomes possible to pinpoint the acoustic emission source location using just one AE sensor. Initial experimental validation conducted on a steel plate demonstrated the method's validity, achieving approximately 90% accuracy in source localization. This technique not only streamlines the process by eliminating the need for multiple sensors but also provides reliable source localization results in dispersive mediums.

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Section: Damage Detection In Dispersive Mediamentioning

confidence: 99%

mentioning

confidence: 99%

Multi-frequency MEMS sensor for single-point source localization in dispersive media using modal acoustic emission and wavelet transformation

Taha,

Zhang,

Khan

et al. 2024

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2024

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“…There are many approaches in the literature to estimate the source position given the TOAs [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ], usually based on the minimization of different choices of cost function that may receive as input the measured TOAs or the time-differences of arrival (TDOAs, defined as the difference of TOAs measured by different sensors). One of the main contributions of this paper is to compare different cost functions and show that they do not usually lead to optimal estimates of source localization in general, except in the particular situation of small noise variance.…”

Section: Introductionmentioning

confidence: 99%

Optimal Passive Source Localization for Acoustic Emissions

Prete

Nascimento

Lopes

2021

Entropy

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Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.

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“…While the first version of our method uses a least-squares approach, the second one considers the source signal is sparse in a known dictionary. The redundancy of information carried by acoustic emission signals is exploited using sparse reconstruction methods in [13,59], and the wavelet transform is widely used in acoustic emission [9,[60][61][62] due to the sparsity of acoustic emission signals in wavelet dictionaries. This motivates us to assume that the signal emitted by the source is sparse in a known dictionary to improve our localization method.…”

Section: Waveform-based Source Localizationmentioning

confidence: 99%

“…Most acoustic emission methods extract the time of arrival (TOA) from the hits that belong to an event and use them to estimate the source position [7][8][9][10][11][12]. In these TOA-based methods, the estimated TOAs depend on the sampled signals, and the estimated position only depends on these estimated TOAs.…”

Section: Introduction To Acoustic Emission Testingmentioning

confidence: 99%

Acoustic emission signal processing.

Junior¹,

Augusto²

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Acoustic Emission is a widely used structure health monitoring (SHM) method for monitoring large structures with high sensitivity. When an acoustic source is active (for example, during the expansion of a crack), it emits an elastic wave that reaches the sensors that are spread along the structure surface. The signals sampled by these sensors are processed and used to estimate the source position.In this work, we study acoustic emission techniques based on the time of arrival (TOA) of the signals received by the sensors and develop methods to improve the source position estimate. More specifically, we derive the probability distribution of the TOA measured by the fixed threshold method, a popular TOA estimation algorithm, as well as an expression for its bias and consequently a TOA debiasing method. Moreover, we derive a nearly-optimal TOA-based source position estimator. Algorithms for anisotropic structures are also investigated.In scenarios where multiple sources are active simultaneously, it is important to group signals (hits) from the same source to avoid using signals emitted by other sources in the localization algorithm. For this reason, we develop hit grouping techniques and compare them with existing methods.We also create a source localization algorithm that directly uses the signals received by the sensors instead of TOAs to estimate the source position. This method takes into account the wave propagation model and also the sparsity of the source signal in a known dictionary to improve the localization performance using sparse reconstruction methods.This work was partially supported by EMBRAER, which provided data of actual acoustic emission tests in complex structures.

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Acoustic Emission Source Localization in Plate-Like Structure

Cited by 8 publications

References 7 publications

Multi-frequency MEMS sensor for single-point source localization in dispersive media using modal acoustic emission and wavelet transformation

Multi-frequency MEMS sensor for single-point source localization in dispersive media using modal acoustic emission and wavelet transformation

Optimal Passive Source Localization for Acoustic Emissions

Acoustic emission signal processing.

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