SYMPOSIA PAPER Published: 01 January 1981
STP27604S

Pattern-Recognition Methods for Classifying and Sizing Flaws Using Eddy-Current Data

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This paper extends the work of Shankar et al to the classification of three types of machined defects in Inconel 600 steam-generator tubing: electrodischarge machined slots, uniform thinning, and elliptical wastage. Three different pattern-recognition techniques were used for classification: (1) an empirical Bayes procedure, (2) a nearest-neighbor algorithm, and (3) a multicategory linear discriminate function. The three types of defects were classified correctly with an overall accuracy of 96 to 98 percent depending on the technique used. Two pattern-recognition algorithms, least squares and nearest neighbor, were used to size uniform-thinning defects in steam-generator tubing. All of the defects were between 25 and 75 percent of the wall in depth. With the least-squares algorithm, we achieved a fit correlation of 0.99 with a 95 percent confidence interval of (0.98, 1.00).

Author Information

Doctor, PG
Battelle, Pacific Northwest Laboratories, Richland, Wash.
Harrington, TP
Battelle, Pacific Northwest Laboratories, Richland, Wash.
Davis, TJ
Battelle, Pacific Northwest Laboratories, Richland, Wash.
Morris, CJ
Battelle, Pacific Northwest Laboratories, Richland, Wash.
Fraley, DW
Battelle, Pacific Northwest Laboratories, Richland, Wash.
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Details
Developed by Committee: E07
Pages: 464–483
DOI: 10.1520/STP27604S
ISBN-EB: 978-0-8031-4792-8
ISBN-13: 978-0-8031-0752-6