SIGNAL-DETECTION AND NOISE SUPPRESSION USING A WAVELET TRANSFORM SIGNAL PROCESSOR - APPLICATION TO ULTRASONIC FLAW DETECTION

The utilization of signal processing techniques in nondestructive test ing, especially in ultrasonics, is widespread. Signal averaging, match ed filtering, frequency spectrum analysis, neural nets, and autoregres sive analysis have all been used to analyze ultrasonic signals. The Wa velet Transform (WT) is the most recent technique for processing signa ls with time-varying spectra. Interest in wavelets and their potential applications has resulted in an explosion of papers; some have called the wavelets the most significant mathematical event of the past deca de. In this work, the Wavelet Transform is utilized to improve ultraso nic flaw detection in noisy signals as an alternative to the Split-Spe ctrum Processing (SSP) technique. In SSP, the frequency spectrum of th e signal is split using overlapping gaussian passband filters with dif ferent central frequencies and fixed absolute bandwidth. A similar app roach is utilized in the WT, but in this case the relative bandwidth i s constant, resulting in a filter bank with a self-adjusting window st ructure that can display the temporal variation of the signal's spectr al components with varying resolutions. This property of the WT is ext remely useful for detecting flaw echoes embedded in background noise. The detection of ultrasonic pulses using the wavelet transform is desc ribed and numerical results show good detection even for signal-to-noi se ratios (SNR) of -15 dB. The improvement in detection was experiment ally verified using steel samples with