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 language | English |
|---|---|
| Pages (from-to) | 329-336 |
| Number of pages | 8 |
| Journal | NDT and E International |
| Volume | 35 |
| Issue number | 5 |
| DOIs | |
| State | Published - Jul 2002 |
| Externally published | Yes |
Funding
This research was supported by a grant from the Electric Power Research Institute under contract WO-S533. The assistance of Mr Jim Benson, the Project Manager, is gratefully acknowledged. The authors also wish to thank Mr Steve Brown for providing the data used in this study, as well as for his guidance.
| Funders | Funder number |
|---|---|
| Electric Power Research Institute | WO-S533 |
Keywords
- Automated analysis
- Bobbin coil
- Defect detection
- Eddy current
- GLRT test