Damage Pattern Recognition of Refractory Materials Based on BP Neural Network

The determination of the damage mode and the quantitative description of the damage of the clustered acoustic emission (AE) signal of the refractory materials based on the BP (back propagation) Neural Network are the subjects of this paper. In this paper, a large number of AE signals in the process...

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Published inNeural Information Processing pp. 431 - 440
Main Authors Liu, Changming, Wang, Zhigang, Li, Yourong, Li, Xi, Song, Gangbing, Kong, Jianyi
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2012
SeriesLecture Notes in Computer Science
Subjects
Acoustic emission
BP neural network
Pattern recognition
Refractory materials
Online AccessGet full text
ISBN9783642344770
3642344771
ISSN0302-9743
1611-3349
DOI10.1007/978-3-642-34478-7_53

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Abstract The determination of the damage mode and the quantitative description of the damage of the clustered acoustic emission (AE) signal of the refractory materials based on the BP (back propagation) Neural Network are the subjects of this paper. In this paper, a large number of AE signals in the process of a three-point bending test were studied and the pattern recognition system of refractory materials based on BP neural network was established with the AE characteristic parameters such as amplitude, counts, rise time, duration and centroid frequency etc. The results show that the total recognition rate of material damage types with this method is as high as 97.5%, and the prediction error of the extent of the damage is about 5%, which indicates that this method has the value of application and dissemination in the aspect of micro-damage pattern recognition and extent prediction of the damage.
AbstractList The determination of the damage mode and the quantitative description of the damage of the clustered acoustic emission (AE) signal of the refractory materials based on the BP (back propagation) Neural Network are the subjects of this paper. In this paper, a large number of AE signals in the process of a three-point bending test were studied and the pattern recognition system of refractory materials based on BP neural network was established with the AE characteristic parameters such as amplitude, counts, rise time, duration and centroid frequency etc. The results show that the total recognition rate of material damage types with this method is as high as 97.5%, and the prediction error of the extent of the damage is about 5%, which indicates that this method has the value of application and dissemination in the aspect of micro-damage pattern recognition and extent prediction of the damage.
Author Liu, Changming
Song, Gangbing
Li, Yourong
Wang, Zhigang
Li, Xi
Kong, Jianyi
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Snippet The determination of the damage mode and the quantitative description of the damage of the clustered acoustic emission (AE) signal of the refractory materials...
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StartPage 431
SubjectTerms Acoustic emission
BP neural network
Pattern recognition
Refractory materials
Title Damage Pattern Recognition of Refractory Materials Based on BP Neural Network
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