Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission

Barat, V. A.; Marchenkov, A. Yu.; Kritskiy, Dmitry; Bardakov, V. V.; Karpova, Marina; Kuznetsov, M.; Zaprudnova, Anastasia; Ushanov, Sergey; Elizarov, S. V.

doi:10.3390/app11083420

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In [25], it was shown that the distribution of AE signal amplitudes corresponds to the Weibull law W(λ,k). The parameters of scale λ and shape k are random values corresponding to the normal distribution law for each of the inspected materials.…”

Section: Ae Source Modelingmentioning

confidence: 99%

“…Considering the results of the preliminary study, a hierarchical two-level algorithm is proposed for modeling the AE source (Figure 4), in which the distribution parameters for λ, k and D a are estimated first, and then the parameters of the AE source are determined, characterizing its emissivity and amplitude values of radiated AE signals. In [25], it was shown that the distribution of AE signal amplitudes corresponds to the Weibull law W(λ,k). The parameters of scale λ and shape k are random values corresponding to the normal distribution law for each of the inspected materials.…”

Section: Ae Source Modelingmentioning

confidence: 99%

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Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

et al. 2021

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Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing.

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Section: Ae Source Modelingmentioning

confidence: 99%

Section: Ae Source Modelingmentioning

confidence: 99%

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

et al. 2021

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Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method

Barat,

Marchenkov,

Bardakov

et al. 2024

Applied Sciences

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The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases.

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Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission

Cited by 2 publications

References 18 publications

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method

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