Analytical Modeling of Acoustic Emission Signals in Thin-Walled Objects

Barat, V. A.; Terentyev, D. A.; Bardakov, V. V.; Elizarov, S. V.

doi:10.3390/app10010279

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…Figure 10 shows more realistic data: modeled signals are shown against the background of noise caused by the pump during hydraulic loading. The correctness of AE signal simulation and their correspondence to the experimental data are demonstrated in [26]. Examples of AE signal waveforms obtained as a result of modeling are shown in Figures 9 and 10.…”

Section: Ae Signal Simulation Resultsmentioning

confidence: 70%

“…Various analytical, semi-analytical or numerical models may be used to simulate the signal shape. In this study, to calculate the acoustic waveguide, an analytical algorithm for calculating the normal waves propagation [26] was used, based on the modal analysis [27] and analytical calculation of the frequency-dependent attenuation coefficient [28]. The modeling of the acoustic path (Figure 5) is described in detail in [26].…”

Section: Modeling the Parameters Of The Acoustic Waveguidementioning

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 Signal Simulation Resultsmentioning

confidence: 70%

Section: Modeling the Parameters Of The Acoustic Waveguidementioning

confidence: 99%

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

et al. 2021

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“…where A S0 , B S0 , A A0 , B A0 -coefficients that are determined by the type of Lamb wave, relative plate thickness, and Poisson's ratio. Analytical expressions of the coefficients are given in [22], their values depend on frequency f and thickness of the testing structure h. The analytical method based on the modal analysis, supplemented by analytical consideration of the attenuation coefficient and the impulse response of AE sensor, allows simulating AE signals of a realistic form [23]. Figure 6 represents the calculated signal (Figure 6a) and experimental signal obtained using the Hsu-Nielsen simulator (Figure 6b) for wall thickness 20 mm at 4 m from the AE sensor.…”

Section: Ae Waveguide Characterizationmentioning

confidence: 99%

Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission

Barat

Marchenkov

Kritskiy³

et al. 2021

Applied Sciences

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The article is devoted to the organization of the structural health monitoring of a walking dragline excavator using the acoustic emission (AE) method. Since the dragline excavator under study is a large and noisy industrial facility, preliminary prospecting researches were carried out to conduct effective control by the AE method, including the study of AE sources, AE waveguide, and noise parameters analysis. In addition, AE filtering methods were improved. It is shown that application of the developed filtering algorithms allows to detect AE impulses from cracks and defects against a background noise exceeding the useful signal in amplitude and intensity. Using the proposed solutions in the monitoring of a real dragline excavator during its operation made it possible to identify a crack in one of its elements (weld joint in a dragline back leg).

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“…A simple scheme of the dynamic indentor-rock system, which is affected by two crucial measurable input variables-pressure force F (N) and revolutions n (rpm), is shown in Figure 2 [33][34][35][36][37].…”

Section: Theoretical Background and Calculationmentioning

confidence: 99%

Statistical Process Control Charts Applied to Rock Disintegration Quality Improvement

et al. 2020

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At present, ever higher demands are placed on the quality of products. The success of organizations in the global market depends mainly on measuring and evaluating their products quality. A set of measurable criteria usually determines product quality. There are many technological processes in the structure of a production organization that is statistically unstable. The norms of ISO class 9000 emphasize statistical process control, known as SPC (Statistical Process Control). They represent a methodology for eliminating the causes of instability of production or technological process. The paper deals with the application of control charts for the technological process of rock disintegration by rotary drilling. The measured values of the dynamic system drilling tool-rock in working mode are processed. The control charts are applied to the input (control) variables of the pressure force-F (N), revolutions-n (rpm), and the output measured variable of the vibration signal of the acceleration. The article constructs and presents the resulting important control charts for the technological process of rock disintegration by rotary drilling. It is essential that for the technological process of rock drilling, the variables that enter and exit the dynamic system must be statistically manageable. The stable state of the input technical parameters (revolutions and pressure force) of the drilling tool is essential from the technological, performance, and economic point of view. The stable state of the output parameters is of significant importance in preventing the emergency state, excessive wear of the drilling equipment and optimizing the optimal operating mode. Industrial practice points out that the correct application of statistical regulation stabilizes the technological process, increasing the quality and productivity of work.

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Analytical Modeling of Acoustic Emission Signals in Thin-Walled Objects

Cited by 13 publications

References 15 publications

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

Structural Health Monitoring of Walking Dragline Excavator Using Acoustic Emission

Statistical Process Control Charts Applied to Rock Disintegration Quality Improvement

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