Extraction of information from acoustic vibration signals using Gabor transform type devices

Abstract

A technique for interpreting acoustic signals is presented based upon the use of Gabor type devices. This interpretation technique processes the information acquired from vibration sensors and presents the information as three time-varying parameters which represent the dominant signal frequency, the nominal breadth of the frequency content of the signal and its effective energy content. The technique is an extension of the chromatic analysis used successfully in the optical domain. The information produced by this analysis proves to be more convenient for defining a signal than that yielded by the conventional Fourier transform approach. By way of an example, the Gabor transform based technique is applied to the monitoring of acoustic signals generated by a high-voltage circuit breaker (as used on power transmission networks). Results are presented which indicate that the technique is capable of quantifying trends in complicated time-varying acoustical signals.

Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines

“…In this particular case the spectral information was obtained using the fast Fourier transform, but there exist other techniques for the extraction of this kind of information which can be simpler and equally effective (using, for example, the Gabor Table 6 Comparison of performance on problem 2-filtered data Channel (Fig. 3 transform as discussed in [17]). These kinds of techniques seem to be most suited for practical schemes like the one presented in this document since these can simplify the implementation of the classifiers even more.…”

Nanomaterials and Their Application

“…Thermal equilibrium between the waves could not be established [58]. This approximation indicates that the energy content of each wave would remain constant.…”

Feature Extraction of the Rub-Impact Rotor System by Means of Wavelet Analysis

“…Although the wavelet transform is similar to the short Fourier transform in a certain sense, the difference between them exists. Compared with the short Fourier transform [10], whose time-frequency resolution is constant, the time-frequency resolution of the wavelet transform depends on the frequency of the signal. At high frequencies, the wavelet transform gives a high time resolution but a low frequency resolution.…”