Proceedings of the AASRI Winter International Conference on Engineering and Technology (AASRI-WIET 2013)

Crack Identification of Drawing Parts Based on Loccal Wave Demomposition and Neural Network

Authors
Zhigao Luo, Qiang Chen, Xin He
Corresponding Author
Zhigao Luo
Available Online December 2013.
DOI
https://doi.org/10.2991/wiet-13.2013.18How to use a DOI?
Keywords
Acoustic emission; Local wave; Back-propagation neural network; Drawing parts; Crack
Abstract
This paper relates to local wave decomposition and back-propagation (BP) neural network.With local wave method, an arbitrary acoustic emission signal can be decomposed efficiently and accurately into a set of intrinsic mode functions (IMFs) and a residual trend. The energy feature parameters extracted from IMFs were employed as the input parameters of the neural network to identify the acoustic emission signals of drawing parts.The experimental results showed this method was effective for crack identification of drawing parts.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2013
ISBN
978-90786-77-95-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/wiet-13.2013.18How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zhigao Luo
AU  - Qiang Chen
AU  - Xin He
PY  - 2013/12
DA  - 2013/12
TI  - Crack Identification of Drawing Parts Based on Loccal Wave Demomposition and Neural Network
PB  - Atlantis Press
SP  - 79
EP  - 82
SN  - 1951-6851
UR  - https://doi.org/10.2991/wiet-13.2013.18
DO  - https://doi.org/10.2991/wiet-13.2013.18
ID  - Luo2013/12
ER  -