Accurate and consistent determination of flawed regions in piping is becoming increasingly important as nuclear power plants age and repair costs increase. Automatic signal classification schemes have the potential to provide consistent and accurate interpretation of inspection data. The feasibility of employing neural network based signal classification systems for the interpretation of ultrasonic weld inspection signals has been demonstrated as part of this work. A windows-based software package was developed. The analysis software uses two techniques, namely, principal component analysis and discrete wavelet transform (DWT) analysis for interpreting A-scan and C-scan image data. The analysis is frequency independent and uses spatial orientation information during processing. The C-scan images contained inspection data from intergranular stress corrosion cracking (IGSCC) samples and were generated using manually acquired data and data from automated scanners at inspection frequencies of 2.25 and 5 MHz. The neural network was trained with signals from the training database and validated by scanning austenitic pipe sections containing IGSCC that had been removed from service. All of the IGSCC was detected indicating the value of using neural networks for automated ultrasonic data analysis in the near future.
|Original language||English (US)|
|Number of pages||7|
|Journal||American Society of Mechanical Engineers, Pressure Vessels and Piping Division (Publication) PVP|
|State||Published - Dec 1 2000|
All Science Journal Classification (ASJC) codes
- Mechanical Engineering