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.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Condensed Matter Physics
- Mechanical Engineering