Performance of consistency parameters analysis using fourier and wavelet transform on multi spectral fluctuation signal

Fluctuation is one of phenomenons resulted in a data acquisition process that has some characteristics such as nonlinear and non-stationary. This study aims to analyze the signal fluctuations generated by MFCS (multi-frequency capacitive sensor) by implementing a combination method of Fourier and wavelet transforms. We apply the statistical approach to analyze, such as: mean, standard deviation and VMR (variance to mean ratio). Mother wavelet transform used Coiflet level 1 and level 2. Furthermore, data processing consists of several categories based on the type of transformation used, namely: (i). TF (Fourier-transformation), (ii). TFW-C11 (Fourier-wavelet transformation) Coiflet-1 (iii). TFW-C12 (Fourier-wavelet Transformation) Coiflet-2. The results obtained reveal that the value of consistency fluctuation that is smaller than one has the highest percentage of the mean values by applying TFW-C12. We notice that this quite new approach can be one of the promising signal processing methods for MFCS sensor.