scholarly journals Acoustic emission signal source localization on plywood surface with cross-correlation method

2018 ◽  
Vol 64 (2) ◽  
pp. 78-84 ◽  
Author(s):  
Yang Li ◽  
Shuai-Shuai Yu ◽  
Li Dai ◽  
Ting-Fang Luo ◽  
Ming Li
Keyword(s):  
Acoustic Emission ◽  
Source Localization ◽  
Acoustic Emission Signal ◽  
Cross Correlation ◽  
Correlation Method ◽  
Signal Source ◽  
Emission Signal

Burst-Type Acoustic Emission Source Localization Based on Multi-Scale and Multi-Sensors

2013 ◽  
Vol 380-384 ◽  
pp. 2954-2957
Author(s):  
Xiao Tao Zhang ◽  
Li Wei Tang ◽  
Ping Wang ◽  
Xing Xing Han ◽  
Shi Jie Deng
Keyword(s):  
Acoustic Emission ◽  
Time Delay ◽  
Source Localization ◽  
Acoustic Emission Signal ◽  
Emission Source ◽  
Scale Analysis ◽  
Emission Signal ◽  
Multi Scale ◽  
Acoustic Emission Source ◽  
Multi Scale Analysis

For the burst-type acoustic emission signal, time delay estimation of two sensors calculated by signal cross-correlation is not accurate, and it leads the source localization results is not also accurate. A new method is proposed to improve accuracy of source localization results based on multi-scale analysis and multi-sensors. Acoustic emission signal multi-scale analysis using wavelet transform, then the most accurate time delay is selected in sub-band of signal multi-scale analysis by multi-sensors time delay vector close rule. Finally, the simulation acoustic emission source localization experiment has high accuracy.

A Method of Leakage Source Localization for Pipeline Based on Acoustic Emission Signal

ICPTT 2013 ◽  
2013 ◽  
Author(s):  
Bixia Pan ◽  
Changhang Xu ◽  
Guoliang Cao ◽  
Guoming Chen ◽  
Huandi Shi
Keyword(s):  
Acoustic Emission ◽  
Source Localization ◽  
Acoustic Emission Signal ◽  
Emission Signal

scholarly journals Acoustic emission signal source separation for a flank wear estimation of drilling tools

Procedia CIRP ◽  
2019 ◽  
Vol 79 ◽  
pp. 57-62 ◽  
Author(s):  
Fritz Klocke ◽  
Benjamin Döbbeler ◽  
Thomas Pullen ◽  
Thomas Bergs
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Flank Wear ◽  
Source Separation ◽  
Signal Source ◽  
Emission Signal ◽  
Drilling Tools

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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