DETECTION OF ULTRASONIC ANOMALY SIGNALS USING WAVELET DECOMPOSITION

A major limitation facing the ultrasonic evaluation of materials is th e high level of background noise from unresolvable grain boundaries th at open mask the reflections from the target of interest in the measur ed signal. The split spectrum processing (SSP) technique, which is bas ed on frequency diversity concepts, has been established as an effecti ve method of achieving anomaly enhancement and grain noise suppression . An alternate decomposition which promises improved resolution capabi lities at high frequencies for the purpose of detecting closely spaced multiple targets was presented as a natural extension to conventional SSP. In this work, wavelet decomposition and reconstruction algorithm s are used to achieve a constant-Q decomposition of the signal. In rec ent years, wavelet techniques have emerged as useful tools in signal a nalysis because of their time-frequency localization properties. Two i mplementations based on the wavelet transform are presented here: dire ct implementation, which is similar to the split spectrum processing f ilter bank realization; and the discrete wavelet transform (DWT) which is implemented using computationally efficient pyramidial structures. Nonlinear algorithms are used to obtain the output signal from the re constructed signals. Preliminary results indicate that these methods a re quite successful in the detection of single targets, but not as eff ective as split spectrum processing in the resolution of closely space d multiple targets.