A generalized likelihood ratio technique for automated analysis of bobbin coil eddy current data

M. Das, H. Shekhar, X. Liu, R. Polikar, P. Ramuhalli, L. Udpa, S. Udpa

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

This paper presents a generalized likelihood ratio technique for detection of defect locations from bobbin coil eddy current data. First a Neyman-Pearson (NP) decision rule for detection of known random signals (in presence of noise) is discussed. The result is then generalized to the problem of detection of unknown random signals that are commonly found in bobbin coil eddy current data. The performance of the proposed detection technique is tested on several real world data sets collected from the steam generator tubes of nuclear power plants. The experimental results indicate that the method is quite promising and useful for automated processing and classification of eddy current data.

Original languageEnglish (US)
Pages (from-to)329-336
Number of pages8
JournalNDT and E International
Volume35
Issue number5
DOIs
StatePublished - Jul 1 2002

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanical Engineering

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