Development of a Method for Generating Material Input Flow for Transport Conveyor Using Experimental Data

Abstract

This work is devoted to the development of a method for generating values of the input material flow of a transport conveyor based on experimental data. The experimental data are represented by a single realization of the material flow for a sufficiently large observation time interval. The statistical characteristics of the implementation of the input material flow are studied. To determine the values of the correlation function, the numerical integration method was used. To analyze statistical characteristics, dimensionless parameters are introduced that can be used to construct similarity criteria for input material flows. When constructing the generator of the input material flow, the canonical expansion of the random process in orthogonal functions is used. This decomposition allows transformations to be carried out over a stochastic input flow of material. It is assumed that the implementation of the input material flow is formed for the steady state of material extraction. As a zero approximation when constructing generators of the input material flow values, it is stipulated that random measurements in the canonical expansion have a normal distribution law. Orthogonal functions are represented by a normalized Fourier series. It is shown that centered random variables of the canonical expansion have dispersion values that are defined as expansion coefficients of the correlation function in a Fourier series. Analysis of the generated material flow realization shows that its values have a distribution close to the normal distribution. An example of realization using a random value generator for the input material flow is presented. The accuracy of the realization is determined by the number of terms in the Fourier series expansion and the accuracy of the numerical integration method.