Detection of ultrasonic flaw signals using wavelet transform techniques

Ultrasonic detection and identification of flaws embedded in large-grained materials is often limited by the presence of high amplitude interfering echoes due to unresolvable grain boundaries. The split spectrum processing (SSP) technique using nonlinear algorithms is very effective in grain noise suppression and flaw detection. The wavelet transform technique is used to perform spectral decomposition, followed by the application of various nonlinear algorithms to obtain the output signal. The wavelet transform is based on the principle of constant Q or constant relative bandwidth frequency. Experimental results for the constant-Q SSP technique are presented. The experimental data indicate improved performance in identifying and extracting multiple targets compared to the conventional fixed bandwidth SSP