2011
EMSS 2011 Proceeding
Automatic Selection of Relevant Data During Ultrasonic Inspection
Authors: Thouraya Merazi Meksen, Malika Boudraa, Bachir Boudraa
Abstract
In recent years, research concerning the automatic interpretation of data from non destructive testing (NDT) is being focused with an aim of assessing embedded flaws quickly and accurately in a cost effective fashion. This is because data yielded by NDT techniques or procedures are usually in the form of signals or images which often do not present direct information of the condition of the structure. Signal processing has provided powerful techniques to extract from ultrasonic signals the desired information on material characterization, sizing and defect detection. The imagery available can add additional and significant dimension in NDT information and for exploiting information. The task of this work is to minimize the volume of data to process replacing ultrasonic images type TOFD by sparse matrix, as there is no reason to store and operate on a huge number of zeros. A combination of two types of neural networks, a perceptron and a Self Organizing Map of Kohonen is used to distinguish between a noise signal from a defect signal in one hand, and to select the sparse matrix elements which correspond to the locations of the defects in the other hand. This new approach to data storage will provide an advantage for the implementations on embedded systems.
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