Abstract
The task is considered related to the creation of monitoring systems in the industrial and environmental technologies that allow to conduct continuous analysis of dust in the industrial premises and quality control of the products made of powders, charges, and other dispersed and composite materials. The proposed methodology is based on the acoustic spectral-timbre control. It allows to make an operational analysis of the physical and chemical properties of the dispersed systems. Using a spectrum analyzer, the acoustic signal is decomposed into a Fourier spectrum consisting of the fundamental and timbre harmonics. The higher harmonics of the spectrum provide for more complete information about the amplitude-frequency parameters of the dispersed system and allow to distinguish by classes of the dispersed composition. The graphical representation of the division of the information space into classes with optimal characteristics allows to interpolate and visually evaluate the belonging of the analyzed spectra to the family with the parameters corresponding to these dispersed systems. When dividing the information space into standard classes, the boundaries between them are approximated by Hermite polynomials with the preservation of the first terms. This makes it possible to divide the modes of dust flows into concentration classes, as well as formally separate them from each other by constructing separating boundaries. After calculating the coefficients of the separating functions, the separating boundaries are determined, which, by virtue of the accepted agreement on the basis functions, will be piecewise linear. The coefficients of the separating functions are calculated according to the developed program of differential diagnostics by the method of potential functions, which implements this method from the theory of pattern recognition. Patterns to be recognized and classified using an automatic recognition system must have a set of measuring statistical characteristics of the spectrum of acoustic emission signals of the dust flow. Pattern recognition systems allow to configure the measurement system to recognize the required concentration classes based on the collected information.
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