Application of the wavelet transform in mechanical spectroscopy and in Barkhausen noise analysis

Abstract

In this work, the wavelet transform and the Fast Fourier Transform FFT are introduced to analyze the quality of the strain response signal in order to improve the accuracy of computation of both the logarithmic decrement and the mechanical loss angle. The wavelet transform of the strain response signal yields a three-dimensional representation (the time-scale joint representation), i.e. space (time), frequency (scale) and amplitude. To emphasize that not only time but also frequency content of the strain response signal collected from a mechanical spectrometer is identified, the author suggests that the time-scale joint representation of the strain response signal should be called ‘Identified Strain Response Signal — ISRS’. The wavelet transform was shown to be an excellent tool to test the quality of the strain and/or of the stress signal in a mechanical spectrometer working in a resonant or subresonant mode. A new approach to the non-stationary Barkhausen noise (BN) signal based on the wavelet transform is also presented. The BN level is usually expressed with the magnetoelastic parameter MP which is a relative number proportional to the root mean square level of the BN. It was found that the MP value is not a reliable parameter for the identification of stress concentrations in ferromagnetic materials. Indeed, a stress concentration resulting from internal stresses can be successfully revealed by the time-scale joint representation of the BN signal. The author suggests that the wavelet transform of the non-stationary RN signal should be called the ‘Identified Barkhausen Noise — IBN’. This approach produces a physically reliable relationship between the IBN and either the level of internal stresses or fine variations in the microstructure.