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Proceedings Paper

Invariance algorithms for processing NDE signals
Author(s): Shreekanth Mandayam; Lalita Udpa; Satish S. Udpa; William Lord
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Paper Abstract

Signals that are obtained in a variety of nondestructive evaluation (NDE) processes capture information not only about the characteristics of the flaw, but also reflect variations in the specimen's material properties. Such signal changes may be viewed as anomalies that could obscure defect related information. An example of this situation occurs during in-line inspection of gas transmission pipelines. The magnetic flux leakage (MFL) method is used to conduct noninvasive measurements of the integrity of the pipe-wall. The MFL signals contain information both about the permeability of the pipe-wall and the dimensions of the flaw. Similar operational effects can be found in other NDE processes. This paper presents algorithms to render NDE signals invariant to selected test parameters, while retaining defect related information. Wavelet transform based neural network techniques are employed to develop the invariance algorithms. The invariance transformation is shown to be a necessary pre-processing step for subsequent defect characterization and visualization schemes. Results demonstrating the successful application of the method are presented.

Paper Details

Date Published: 15 November 1996
PDF: 7 pages
Proc. SPIE 2944, Nondestructive Evaluation of Materials and Composites, (15 November 1996); doi: 10.1117/12.259060
Show Author Affiliations
Shreekanth Mandayam, Iowa State Univ. (United States)
Lalita Udpa, Iowa State Univ. (United States)
Satish S. Udpa, Iowa State Univ. (United States)
William Lord, Iowa State Univ. (United States)


Published in SPIE Proceedings Vol. 2944:
Nondestructive Evaluation of Materials and Composites
Steven R. Doctor; Carol A. Nove; George Y. Baaklini, Editor(s)

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