A novel Bayesian approach to acoustic emission data analysis

Agletdinov, E. A.; Pomponi, Eraldo; Мерсон, Д. Л.; Vinogradov, Alexei

doi:10.1016/j.ultras.2016.07.014

Cited by 22 publications

(14 citation statements)

References 47 publications

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“…Due to the influence of the material microstructure on the values of AE parameters, it is assumed that the estimated parameters will be random and evaluated using a probability distribution. This assumption, as well as the results obtained, is consistent with the Bayesian statistical approach proposed by Vinogradov in [23].…”

Section: Introductionsupporting

confidence: 91%

Estimation of Fatigue Crack AE Emissivity Based on the Palmer–Heald Model

Barat

Marchenkov

Elizarov³

2019

Applied Sciences

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This article is devoted to materials testing by the acoustic emission (AE) method, which is the analysis of models and diagnostic parameters to assess the probability of detection of a defect in steel structures. The paper proposes to evaluate the emissivity of the material quantitatively by the number and dynamics of the accumulation of acoustic emission impulses. Experimental studies were carried out on pearlitic structural steels, including the loading of samples with fatigue cracks. It was established that the number of AE impulses emitted during loading of an object with a fatigue crack is a random variable corresponding to the normal distribution law. The results show that an estimate of the number of AE impulses emitted during the loading of samples with fatigue cracks can be obtained by distributing the multiplicative parameter D of the Palmer-Heald model by taking into account the maximum value of the applied load.

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Section: Introductionsupporting

confidence: 91%

Estimation of Fatigue Crack AE Emissivity Based on the Palmer–Heald Model

Barat

Marchenkov

Elizarov³

2019

Applied Sciences

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“…Importantly is that the slopes of the obtained regression lines for the tests performed with different stylus velocities are different by approximately a factor of two (within the experimental scatter) as predicted by Eq. (5). Thus, despite the simplicity, the model provides a reasonable first-order explanation to the observed results.…”

Section: Ss Vw mentioning

confidence: 63%

“…Providing this information with an unprecedented temporal resolution, the modern acoustic emission (AE) technique becomes increasingly recognised in micro-testing, e.g. during indentation [2] or scratch testing of ductile, brittle [3] and coated materials [4,5]. The main challenge for the AE technique is to assess a very low amplitude signal generated in small, micrometres scale, deforming volumes.…”

mentioning

confidence: 99%

Probing elementary dislocation mechanisms of local plastic deformation by the advanced acoustic emission technique

et al. 2018

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To gain a deeper insight into fundamental processes of localised plastic deformation, we propose a new strategy for micro-mechanical testing combining the micro-scale scratching and analysis of low amplitude acoustic emissions (AE) accompanying dynamic dislocation processes in solids. The analysis of the AE spectral density reveals its strong dependence on crystallographic orientations of the grains along the indenter path, thus reflecting the finest features of the local dislocation activity. Adding a temporal dimension with a sub-microsecond resolution, the proposed methodology substantially enhances the existing capacity of micromechanical testing and can be used in a wide range of testing schemes.

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“…In the proposed method, the original AE stream is transformed into a time-dependent parameter φ, which was defined in [59] and used for increasing the signal-to-noise ratio and finding the weak events in seismic time series. It then was shown effective for finding AE events in a noisy time series recorded during micro-indentation testing [60]. Unlike the previous reports, the present work deals with signal detection, and its key novelty lies in the proposed strategy pairing the φ-parameter with a decision function, which is then tuned for precise AE arrival time picking and signal end detection in a robust and reproducible way.…”

Section: Methodsmentioning

confidence: 99%

A New Method of Low Amplitude Signal Detection and Its Application in Acoustic Emission

2019

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A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, integrity assessment of industrial facilities and/or microseismicity studies. In all these cases, the early and reliable detection of possible damage is of paramount importance and is practically limited by detectability of transient signals on the background of random noise. The proposed triggering algorithm is based on a logarithmic derivative of the power spectral density function. It was tested on the synthetic data, which mimics the actual ultrasonic acoustic emission signal recorded continuously with different signal-to-noise ratios (SNR). Considerable advantages of the proposed method over established fixed amplitude threshold and STA/LTA (Short Time Average / Long Time Average) techniques are demonstrated in comparative tests.

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A novel Bayesian approach to acoustic emission data analysis

Cited by 22 publications

References 47 publications

Estimation of Fatigue Crack AE Emissivity Based on the Palmer–Heald Model

Estimation of Fatigue Crack AE Emissivity Based on the Palmer–Heald Model

Probing elementary dislocation mechanisms of local plastic deformation by the advanced acoustic emission technique

A New Method of Low Amplitude Signal Detection and Its Application in Acoustic Emission

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