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An unsupervised Learning Algorithm for Fatigue Crack Detection in Waveguides

2009

Ultrasonic 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-di…

Discrete wavelet transformEngineeringGuided wave testingultrasonic guided wave damage index steel control ndt crackbusiness.industryAcousticsCondensed Matter PhysicsNovelty detectionAtomic and Molecular Physics and OpticsWaveletMechanics of MaterialsNondestructive testingSignal ProcessingOutlierForensic engineeringGeneral Materials ScienceUltrasonic sensorStructural health monitoringElectrical and Electronic EngineeringbusinessSettore ICAR/08 - Scienza Delle CostruzioniCivil and Structural Engineering

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