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  5. A statistical measure for wavelet based singularity detection
 
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A statistical measure for wavelet based singularity detection

Author(s)
Pakrashi, Vikram  
Basu, Biswajit  
O'Connor, Alan  
Uri
http://hdl.handle.net/10197/10586
Date Issued
2009-07-14
Date Available
2019-05-21T13:46:19Z
Abstract
This paper presents a statistical measure for the identification of the presence, the location, and the calibration of the strength of singularity in a signal or in any of its derivatives in the presence of measurement noise without the requirement of a baseline using a wavelet based detection technique. For this proposed wavelet based detection of singularities present in a signal, the problem of false alarm and its significant reduction by use of multiple measurements is presented. The importance of the proposed measure on baseline and nonbaseline damage calibration has been discussed from the aspect of structural health monitoring. The findings in this paper can also be used for crosschecking of background noise level in an observed signal. The detection of the existence, location, and extent of an open crack from the first fundamental modeshape of a simply supported beam is presented as an example problem.
Type of Material
Journal Article
Publisher
The American Society of Mechanical Engineers
Journal
Journal of Vibration and Acoustics
Volume
131
Issue
4
Start Page
0410151
End Page
0410156
Copyright (Published Version)
2009 American Society of Mechanical Engineers
Subjects

Singularity detection...

Wavelet analysis

False alarm

Signal to noise ratio...

Open crack

DOI
10.1115/1.3142880
Language
English
Status of Item
Peer reviewed
ISSN
1048-9002
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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asme vibration and acoustics for pdf_coversheet.pdf

Size

516.22 KB

Format

Adobe PDF

Checksum (MD5)

1ddccf9bc01a90e621015203dbdfea6e

Owning collection
Mechanical & Materials Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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