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

TJ. DavisPG. DoctorDW. FraleyTP. HarringtonCJ. Morris

出版	ASTM International, 1981
URL	http://books.google.com.hk/books?id=Jdd6zgEACAAJ&hl=&source=gbs_api

註釋

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).