Frequency invariant classification of ultrasonic weld inspection signals

Robi Polikar, Lalita Udpa, Satish S. Udpa, Tom Taylor

    Research output: Contribution to journalArticle

    75 Scopus citations

    Abstract

    Automated signal classification systems are finding increasing use in many applications for the analysis and interpretation of large volumes of signals. Such systems show consistency of response and help reduce the effect of variabilities associated with human interpretation. This paper deals with the analysis of ultrasonic NDE signals obtained during weld inspection of piping in boiling water reactors. The overall approach consists of three major steps, namely, frequency invariance, multiresolution analysis, and neural network classification. The data are first preprocessed whereby signals obtained using different transducer center frequencies are transformed to an equivalent reference frequency signal. Discriminatory features are then extracted using a multiresolution analysis technique, namely, the discrete wavelet transform (DWT). The compact feature vector obtained using wavelet analysis is classified using a multilayer perceptron neural network. Two different databases containing weld inspection signals have been used to test the performance of the neural network. Initial results obtained using this approach demonstrate the effectiveness of the frequency invariance processing technique and the DWT analysis method employed for feature extraction.

    Original languageEnglish (US)
    Pages (from-to)614-625
    Number of pages12
    JournalIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    Volume45
    Issue number3
    DOIs
    StatePublished - Dec 1 1998

    All Science Journal Classification (ASJC) codes

    • Instrumentation
    • Acoustics and Ultrasonics
    • Electrical and Electronic Engineering

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