Feature Extraction of the Acoustic Emission Signals of Low Carbon Steel Pitting Corrosion Based on Independent Component Analysis and Wavelet Transform-Reference-Cited by-同舟云学术

Feature Extraction of the Acoustic Emission Signals of Low Carbon Steel Pitting Corrosion Based on Independent Component Analysis and Wavelet Transform

Published:2013-08 Issue: Volume:373-375 Page:677-680
ISSN:1662-7482
Container-title:Applied Mechanics and Materials
language:
Short-container-title:AMM

Author:

Li Wei¹,Gong Yu Li¹,Yu Yang¹

Affiliation:

1. Northeast Petroleum University

Abstract

Based on the characteristics of the acoustic emission (AE) signals from low carbon steel pitting corrosion, a new extraction method was proposed with wavelet transformation and independent component analysis. The experiment result shows that the new method can overcome the influence induced by the uncertainty of the independent source of low carbon steel pitting corrosion and good extraction result can be achieved.

Publisher

Trans Tech Publications, Ltd.

Link

https://www.scientific.net/AMM.373-375.677.pdf

Reference6 articles.

1. Jun Zhang, Yongchang Liu, Qiang Huang. Extraction of the Characteristics of the fault signal of diesel engine based on wavelet analysis. Motorcycle, 2003, 32 (1): 34 ~36.

2. M. Girolami. Self-organizing neural network: Independent component analysis and blind source separation[M]. New York: Springer, 1999, 11～24.

3. A. Hyvarinen: Survey on Independent Component Analysis (Download from: http: / www. cis. hut. fi/ projects/ ICA).

4. Huang Chuang , Shi Hongbo. Process monitoring method based on independent component analysis (ICA) and wavelet transform . Journal of Jilin University (Engineering Science), 2004, 34 (3): 465~ 466.

5. Xiaodong Liu, Shuntian Lou. During the course of blind source separation source identification of dynamic change. Journal of Xi'an University of Electronic Technology (Natural Science), 2003, 30 (5): 598~603.