Predictive analysis of the interval material flow rates in transport conveyors based on experimental data

Abstract

This study examines a method for constructing a generator of random values of the input flow of material to form a training data set in highly efficient transport conveyor models based on a neural network. Dimensionless parameters are introduced that make it possible to represent the model of the input material flow of a transport conveyor in a dimensionless form. Coordinate functions are determined to approximate the experimental realization of the input material flow. A canonical decomposition of the experimental realization of the input material flow in terms of coordinate functions based on the use of fixed intervals is presented. For the selected canonical decomposition of the experimental realization of the input material flow, a theoretical correlation function is determined. It is shown that as the number of intervals increases, the correlation function of the experimental realization tends to the theoretical correlation function. The stages of constructing a random value generator for the input material flow are presented in detail. A comparative analysis of the experimental, approximated and generated realization for the input material flow is presented and estimates of the statistical characteristics of the realizations of the input material flow are given. The correlation functions constructed for the experimental, approximated and generated realizations of the input material flow are analyzed. An estimate is given of the length of the time interval required to carry out experimental changes in the input material flow.