Research on the time difference of arrival location method of an acoustic emission source based on visible graph modelling

Mu, Weilei; Zou, Zhenxing; Sun, Hailiang; Liu, Guijie; Wang, Shoujun

doi:10.1784/insi.2018.60.12.697

Cited by 2 publications

(2 citation statements)

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“…It is important to detect damage in the early stages to prevent catastrophes from occurring [ 2 ]. Mu et al used visible graph modelling [ 3 ] and acoustic emission beamforming [ 4 ] to achieve AE source localization. In terms of material life prediction, Roberts et al linked the AE count rate with crack growth [ 5 ] in an effort to predict the material’s remaining life.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking

Gao

Wang

et al. 2022

Sensors

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The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.

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Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking

Gao

Wang

et al. 2022

Sensors

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“…On the basis of these features, filtered back projection and the algebraic reconstruction technique are proposed [3,4]. However, determining precisely the ToF or amplitude of scattered signals is difficult, and this drawback limits the application of these methods [5].…”

Section: Introductionmentioning

confidence: 99%

Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

Qi¹,

et al. 2020

JMSE

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The reconstruction algorithm for the probabilistic inspection of damage (RAPID) is aimed at localizing structural damage via the signal difference coefficient (SDC) between the signals of the present and reference conditions. However, tomography is only capable of presenting the approximate location and not the length and angle of defects. Therefore, a new quantitative evaluation method called the multiple back propagation neural network (Multi-BPNN) is proposed in this work. The Multi-BPNN employs SDC values as input variables and outputs the predicted length and angle, with each output node depending on an individual hidden layer. The cracks of different lengths and angles at the center weld seam of offshore platforms are simulated numerically. The SDC values of the simulations and experiments were normalized for each sample to eliminate external interference in the experiments. Then, the normalized simulation data were employed to train the proposed neural network. The results of the simulations and experimental verification indicated that the Multi-BPNN can effectively predict crack length and angle, and has better stability and generalization capacity than the multi-input to multi-output back propagation neural network.

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Research on the time difference of arrival location method of an acoustic emission source based on visible graph modelling

Cited by 2 publications

References 0 publications

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking

Modeling and Analysis of Acoustic Emission Generated by Fatigue Cracking

Identification of Crack Length and Angle at the Center Weld Seam of Offshore Platforms Using a Neural Network Approach

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