Next Article in Journal
Using Acoustic Sensors to Improve the Efficiency of the Forest Value Chain in Canada: A Case Study with Laminated Veneer Lumber
Previous Article in Journal
Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawai‘i Using Fine Resolution Remote Sensing and GIS
Previous Article in Special Issue
Measuring Oscillating Walking Paths with a LIDAR
Article Menu

Export Article

Open AccessArticle
Sensors 2011, 11(6), 5695-5715; doi:10.3390/s110605695

Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals

Computational Neuroscience Research Group, Katholieke Universiteit Leuven, Herestraat 49, B-3000 Leuven, Belgium
Previous address: Materials Performance and Non-Destructive Evaluation (NDT), Department of Metallurgy and Materials, Katholieke Universiteit Leuven, Kasteelpark Arenberg 44, B-3001 Heverlee, Belgium.
*
Author to whom correspondence should be addressed.
Received: 8 April 2011 / Revised: 9 May 2011 / Accepted: 23 May 2011 / Published: 27 May 2011
(This article belongs to the Special Issue Advanced Sensing Technology for Nondestructive Evaluation)
View Full-Text   |   Download PDF [1345 KB, uploaded 21 June 2014]   |  

Abstract

The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion. Here we apply wavelet packet decomposition to extract features from acoustic emission signals. We then use the extracted wavelet packet coefficients for distinguishing between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking. The local discriminant basis selection algorithm can be considered as a standard for the selection of the most discriminative wavelet coefficients. However, it does not take the statistical dependencies between wavelet coefficients into account. We show that, when these dependencies are ignored, a lower accuracy is obtained in predicting the corrosion type. We compare several mutual information filters to take these dependencies into account in order to arrive at a more accurate prediction. View Full-Text
Keywords: acoustic emission; chemical process industry; corrosion monitoring; feature subset selection; information theory; mutual information; Wavelet Packet Transform acoustic emission; chemical process industry; corrosion monitoring; feature subset selection; information theory; mutual information; Wavelet Packet Transform
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Van Dijck, G.; Van Hulle, M.M. Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals. Sensors 2011, 11, 5695-5715.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top