Robust clustering of acoustic emission signals using the Kohonen network

Wahid Emamian, Mostafa Kaveh, Ahmed H. Tewfik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Acoustic emission-based techniques are promising for nondestructive inspection of mechanical systems. For reliable automatic fault monitoring, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. In this paper we propose the application of the Kohonen network for this purpose. The principal components of the short-time Fourier transforms of the data were applied input of the network. The clustering results confirm the capability of the Kohonen network for reliable source identification of acoustic emission signals, assuming enough care has been taken in implementing the training algorithm of the network.

Original languageEnglish (US)
Title of host publicationDesign and Implementation of Signal Processing SystemNeural Networks for Signal Processing Signal Processing EducationOther Emerging Applications of Signal ProcessingSpecial Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3891-3894
Number of pages4
ISBN (Electronic)0780362934
DOIs
StatePublished - 2000
Event25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey
Duration: Jun 5 2000Jun 9 2000

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
ISSN (Print)1520-6149

Other

Other25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000
Country/TerritoryTurkey
CityIstanbul
Period6/5/006/9/00

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