An unsupervised learning algorithm for fatigue crack detection in waveguides

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dc.contributor.authorCammarata, Mko
dc.contributor.authorSohn, Hoonko
dc.contributor.authorHarries, Kko
dc.contributor.authorRizzo, Pko
dc.contributor.authorDutta, Dko
dc.date.accessioned2010-06-14T17:17:52Z-
dc.date.available2010-06-14T17:17:52Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2009-02-
dc.identifier.citationSMART MATERIALS & STRUCTURES, v.18, no.2-
dc.identifier.issn0964-1726-
dc.identifier.urihttp://hdl.handle.net/10203/18839-
dc.description.abstractUltrasonic guided waves (UGWs) are a useful tool in structural health monitoring (SHM) applications that can benefit from built-in transduction, moderately large inspection ranges, and high sensitivity to small flaws. This paper describes an SHM method based on UGWs and outlier analysis devoted to the detection and quantification of fatigue cracks in structural waveguides. The method combines the advantages of UGWs with the outcomes of the discrete wavelet transform (DWT) to extract defect-sensitive features aimed at performing a multivariate diagnosis of damage. In particular, the DWT is exploited to generate a set of relevant wavelet coefficients to construct a uni-dimensional or multi-dimensional damage index vector. The vector is fed to an outlier analysis to detect anomalous structural states. The general framework presented in this paper is applied to the detection of fatigue cracks in a steel beam. The probing hardware consists of a National Instruments PXI platform that controls the generation and detection of the ultrasonic signals by means of piezoelectric transducers made of lead zirconate titanate. The effectiveness of the proposed approach to diagnose the presence of defects as small as a few per cent of the waveguide cross-sectional area is demonstrated.-
dc.description.sponsorshipThe support of the University of Pittsburgh to Mr Cammarata through start-up funding available to the first author is acknowledged. The third and fourth authors acknowledge the support of the Korea Science and Engineering Foundation (M20703000015-07N0300-01510) and the Korea Research Foundation (D00462). Experimental work was conducted in the Watkins Haggart Structural Engineering Laboratory at the University of Pittsburgh.en
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherIOP PUBLISHING LTD-
dc.titleAn unsupervised learning algorithm for fatigue crack detection in waveguides-
dc.typeArticle-
dc.identifier.wosid000262582800025-
dc.type.rimsART-
dc.citation.volume18-
dc.citation.issue2-
dc.citation.publicationnameSMART MATERIALS & STRUCTURES-
dc.identifier.doi10.1088/0964-1726/18/2/025016-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorSohn, Hoon-
dc.contributor.nonIdAuthorCammarata, M-
dc.contributor.nonIdAuthorHarries, K-
dc.contributor.nonIdAuthorRizzo, P-
dc.contributor.nonIdAuthorDutta, D-
dc.type.journalArticleArticle-
dc.subject.keywordPlusLAMB WAVES-
dc.subject.keywordPlusOUTLIER ANALYSIS-
dc.subject.keywordPlusMETALLIC STRUCTURES-
dc.subject.keywordPlusNOVELTY DETECTION-
dc.subject.keywordPlusDAMAGE DETECTION-
dc.subject.keywordPlusLASER VIBROMETRY-
dc.subject.keywordPlusPART II-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusSCATTERING-
dc.subject.keywordPlusSTRANDS-
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