Hybrid Digital Signal Processing and Neural Networks for Automated Diagnostics Using Eddy Current Inspection
The primary purpose of the current research is to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination (NDE) data. Specifically, data from eddy current inspection of heat exchanger tubing are utilized to develop this technology. The results of analysis show that for effective (low-error) artifact type classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods.
KeywordsMean Square Error Nondestructive Test Defect Type Defect Parameter Fourier Descriptor
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