Flaw detection in stainless steel samples using wavelet decomposition

Abstract

Wavelet techniques have emerged as useful tools in signal analysis because of their time-frequency localization properties. In this work, wavelet decomposition and reconstruction algorithms are used in ultrasonic nondestructive testing applications to distinguish between the flaw echo and background grain noise. The discrete wavelet transform is applied to reconstruct the signal at scales likely to contain the target. Nonlinear algorithms are used to obtain the output signal from the reconstructed signals. Preliminary results indicate that these methods are quite successful in the detection of single targets but not as effective as split spectrum processing in the resolution of closely spaced multiple targets