Burst Analysis for Multi-Level Leakage Detection in Water-Filled Pipeline Based on Acoustic Emission Signal

2021 ◽  
pp. 93-103
Author(s):  
Bach Phi Duong ◽  
Jaeyoung Kim ◽  
Inkyu Jeong ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Leakage Detection ◽  
Emission Signal ◽  
Burst Analysis ◽  
Multi Level

The Research on Leak Detection Technology of Natural Gas Pipeline Based on EMD

2014 ◽  
Vol 494-495 ◽  
pp. 793-796 ◽  
Author(s):  
Chuan Jiang Li ◽  
Jia Pan Zhang ◽  
Zi Qiang Zhang ◽  
Ju Li Hu ◽  
Yi Li
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Leakage Detection ◽  
Intrinsic Mode Functions ◽  
Emission Signal ◽  
Natural Gas Pipeline ◽  
Detection Technology ◽  
Mode Decomposition ◽  
Leakage Characteristics ◽  
Signal Processing Methods

The acoustic emission signal of pipeline leakage is characterized by nonlinear and non-stationary. It is not feasible to extract the leakage feature signal in traditional signal processing methods. The leak locations can be detected by employing the improved empirical mode decomposition (EMD) to decompose the acoustic emission signal into several intrinsic mode functions (IMF), choosing IMFs containing leakage characteristics to be reconstructed, and doing correlation analysis. Experimental results show that the positioning accuracy of leakage detection is improved obviously.

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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