Neural networks for ultrasonic NDE signal classification using time-frequency analysis

C. H. Chen, Gwo-Giun Lee

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

9 Citations (Scopus)

Abstract

Ultrasonic nondestructive evaluation (NDE) of material defects typically involves signals which are nonstationary in nature. Whether we are doing deconvolution or signal classification, time-frequency analysis, instead of the frequency or time domain analysis alone, is much needed. In this paper, we examine features derived from the Wigner distribution and its derivatives, and features derived from subband coding wavelet decomposition. Both the traditional nearest neighbor decision rule and the neural network classifiers, the backpropagation trained network and the Nestor's RCE network, are considered to classify the ultrasonic pulse echoes into one of three hidden geometrical defect classes. Neural network classifiers using features properly derived from the time-classification results. Although the data set employed is small, the conclusion is fairly consistent with experiments in other large data sets.

Original languageEnglish
Title of host publicationPlenary, Special, Audio, Underwater Acoustics, VLSI, Neural Networks
PublisherPubl by IEEE
ISBN (Print)0780309464
Publication statusPublished - 1993 Jan 1
Event1993 IEEE International Conference on Acoustics, Speech and Signal Processing - Minneapolis, MN, USA
Duration: 1993 Apr 271993 Apr 30

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume1
ISSN (Print)0736-7791

Other

Other1993 IEEE International Conference on Acoustics, Speech and Signal Processing
CityMinneapolis, MN, USA
Period93-04-2793-04-30

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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