Abstract
Most signal processing techniques for NDT methods have been based on Fourier analysis. Since the application of Fourier transform to the analysis of recorded vibrational signals shows characteristics only in frequency domain and not in time domain, it is frequently not sufficient to precisely analyse transient waves. Recently, there have been many researches undertaken to improve signal processing techniques for NDT methods. Especially, the time-frequency analysis named wavelet theory has been recognized to overcome many drawbacks of traditional techniques. The Fourier transform provides insightful results for most stationary signals. Meanwhile, in the analysis of a non-stationary signal, the Fourier transform has some disadvantages. Small changes of a signal may not be realized and the analysis may change depending on the length of data. Moreover, when a signal is converted into the frequency domain by the Fourier transform, the information in the time domain has to be hidden. In order to resolve such limitations in the Fourier transform analysis, several time-frequency analyses have been attempted such as the analytical signal using instantaneous signal parameters, the short-time Fourier transform (time windowing), the windowed Fourier transform (band-pass filters), and the wavelet transform. The wavelet transform of transient signals provides a method for mapping the time domain into the frequency spectrum as a function of time. To verify the availability of wavelet transform as a time-frequency analysis tool, model experiments have been performed on the specially designed concrete lining where defects (e.g., crack and cavity) are artificially made (see Fig. 1). The stress wave is generated by dropping a steel ball and the acquired signal is analysed using wavelet transform as well as Fourier transform. It is found that the contour map by wavelet transform renders more distinct results than power spectrum by Fourier transform. The arrival time of each frequency component needed for estimation of the wave velocity can be easily determined from ridge analysis. Furthermore, the thickness of the concrete lining and the defect location can be effectively estimated using wavelet transform. Therefore, it is concluded that the wavelet transform is an effective analysis tool for detecting defect zones which exist in tunnel concrete linings. (A). Reprinted with permission from Elsevier. For the covering abstract see ITRD E124500.
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