NDTnet 1998 Aug, Vol.3 No.8

Full-Text - Abstract

Recently ultrasonic spectroscopy has become more common NDE technique and specialized instruments are commercially available now for inspection of aerospace structures, ball bearings, and even concrete. The ultrasonic spectroscopy consists in measuring frequency response of the tested object in the frequency range typical for common ultrasonic inspection. The tested object is excited with variable frequency elastic waves generated by the piezoelectric transducer. Separate or the same transducer is used for reception of the elastic waves propagating in the object. The spectrum acquired in this way is used for characterization of the inspected object or detection of flaws in it. Since the spectrum gathered by the modem instrument can consist of several thousand points and can contain many resonance peaks the user demands tools for spectrum classification.

In this paper we present a neural network based spectrum classifier (NSC) and its application to ultrasonic resonance spectroscopy. The use of an Artificial Neural Network (ANN) is proposed to meet the requirements of high sensitivity for small but relevant changes in the spectra, and simultaneous robustness against measurement noise. Provided with enough training examples, the ANNs are known to be able to find features representative for different classes and to generalize in order to cope with the measurement noise. Among several types of ANNs that could be used for classifying the spectra we have chosen a multilayer perceptron (MLP). Although the MLP itself can perform feature extraction, we included an optional pre-processor for this purpose. The NSC is essentially model free and can be trained using real and modeled spectra. The classifier uses both amplitude and phase information in the spectra.

The performance of the classifier has been verified using a number of practical applications, here we present results of its application to detection of disbounds in adhesivly joint multilayer aerospace structures using Fokker Bond Tester resonance instrument. In this case the classifier is capable of detecting very small disbonds (larger than 25% of the sensor area) and correct identifying their position in the structure (identifying the defected joint).

Abstract Source:
Book of Abstracts, 7th European Conference on Non-Destructive Testing, 26-29 May 1998, ISBN: 87-986898-0-00

Full-Text Source:
Proceedings of the 7th European Conference on Non-Destructive Testing, 26-29 May 1998, ISBN:

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