STP722: Pattern-Recognition Methods for Classifying and Sizing Flaws Using Eddy-Current Data

    Doctor, PG
    Senior research scientist, senior research scientist, senior research scientist, research scientist, senior research scientist, Battelle, Pacific Northwest Laboratories, Richland, Wash.

    Harrington, TP
    Senior research scientist, senior research scientist, senior research scientist, research scientist, senior research scientist, Battelle, Pacific Northwest Laboratories, Richland, Wash.

    Davis, TJ
    Senior research scientist, senior research scientist, senior research scientist, research scientist, senior research scientist, Battelle, Pacific Northwest Laboratories, Richland, Wash.

    Morris, CJ
    Senior research scientist, senior research scientist, senior research scientist, research scientist, senior research scientist, Battelle, Pacific Northwest Laboratories, Richland, Wash.

    Fraley, DW
    Senior research scientist, senior research scientist, senior research scientist, research scientist, senior research scientist, Battelle, Pacific Northwest Laboratories, Richland, Wash.

    Pages: 20    Published: Jan 1981


    Abstract

    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).

    Keywords:

    pattern-recognition, eddy current inspection, classification, nondestructive evaluation, signal analysis, Inconel 600 steam generator tubing, clustering


    Paper ID: STP27604S

    Committee/Subcommittee: E07.11

    DOI: 10.1520/STP27604S


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