scholarly journals Using Hilbert-Huang Transform to Process and Analyze the Corrosion Acoustic Emission Signal of the Tank Bottom Plate

2020 ◽  
Vol 508 ◽  
pp. 012161
Author(s):  
Wenxin Jiang ◽  
Ke Gao ◽  
Zhi He ◽  
Meng Pan ◽  
Shijie Zhang
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Bottom Plate ◽  
Hilbert Huang Transform ◽  
Emission Signal ◽  
Tank Bottom

Identify on Acoustic Emission Signals of Tank Bottom Corrosion Using Hidden Markov Model

2012 ◽  
Vol 239-240 ◽  
pp. 42-46
Author(s):  
Yang Yu ◽  
Ming Yu Zhang
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Emission Signal ◽  
Model Library ◽  
Oil Storage ◽  
Oil Storage Tank ◽  
Tank Bottom ◽  
Output Probability

Aiming at the acoustic emission signals of oil storage tank bottom injured, hidden Markov algorithm is proposed to identify the tank bottom corrosion signal. Typical corrosion acoustic emission signal is divided into transient acoustic signal, continuous acoustic emission signal and mixed acoustic emission.Baum-Welch algorithm is used to train these typical corrosion acoustic emission signals model, then establish HMM model library. The forward-backward algorithm is used to compute each acoustic emission model’s output probability. The simulation experiments shows that the hidden Markov algorithm can correctly identified the acoustic emission signals.

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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