Degree Type

Dissertation

Date of Award

2002

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

First Advisor

Satish S. Udpa

Abstract

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This thesis describes an alternative approach which uses the least mean square (LMS) method to determine the coordinates of the ultrasonic probe followed by the use of a synthetic aperture focusing technique (SAFT). The method is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The movement of the probe inside the tube is modeled using spherical and cylindrical coordinate systems. The mean square error (MSE) between the model prediction and the experimentally measured distance between the probe and the tube wall is minimized using the steepest descent algorithm to obtain estimates of the probe canting angle and its location. The information is used in conjunction with the synthetic aperture focusing technique to estimate the location of the ultrasonic reflector. An alternate approach employing a model based deconvolution has been described to facilitate comparison of results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm in conjunction with the Newton-Raphson method to estimate the time of flight. Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented.

DOI

https://doi.org/10.31274/rtd-180813-12121

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu

Copyright Owner

Daewon Kim

Language

en

Proquest ID

AAI3051478

File Format

application/pdf

File Size

114 pages

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