Titlebook: Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials; C. H. Chen Conference proceedings 1988 Springer-Verla

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Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials978-3-642-83422-6Series ISSN 0258-1248

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Non-Destructive Evaluation in the Time-Frequency Domain by Means of the Wigner-Ville Distributionxed tools which consider time and frequency simultaneously. Among different possible approaches, it is justified that time-frequency distributions related to the Wigner-Ville transform are of special interest. A concrete example, stemming from an underwater acoustics experiment, is provided for supporting the usefulness of the proposed method.

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Knowledge Based Systems in Nondestructive Evaluation(a)on Assistant (SIIA) is used as an illustrative example [1]. SIIA is a prototype knowledge based system designed to assist in making the operation of the Synthetic Aperture Focusing Technique (SAFT) Ultrasonic Inspection System more reliable and efficient [2].

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Modern Signal ProcessingIn this paper, we present some aspects of modern signal processing. The issues considered include stochastic modeling, linear prediction, and the interrelationship between them. Computational aspects of these issues are also considered. Detection algorithms are described, which exploit the innovations representation of a stochastic process.

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Pattern Recognition of Ultrasonic Signals for Detection of Wall Thinninges, attenuation parameters used to characterize acousto-ultrasonic signals are less discriminating in metals. This preliminary investigation showed the feasibility of using pattern recognition methods to effectively interpret acousto-ultrasonic signals from sections of metal piping that have been subjected to wall thinning.

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Processing of thermal images for the detection and enhancement of subsurface flaws in composite mate aluminum to foam laminates. Also quantitative characterization of flaws in graphite-epoxy laminates using specially developed image processing and modelling will be presented. Emphasis will be given to either time-domain or space-domain image processing methods for precise and reliable defect visualization.

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