Analysis of paper variability using the continuous wavelet transform

A novel approach for the spectral analysis of paper variability data is presented. The method is based on the continuous wavelet transform using a second order Gaussian basic wavelet. This paper provides the theoretical basis for decomposing one-dimensional data sets into spectral and spatial components. Trigonometric functions are used as model functions to demonstrate the potential of this approach for determining local spectral energy to characterize the scale of profile features, such as flocs, streaks and mean fiber orientation. Several methods for applying the results of wavelet transform analysis to characterize the non-stationarity or dependence of paper variation as a function of position, e.g. cross machine, are discussed. The results of this method are compared with the conventional method of wavelength (formation) spectral analysis.