SUPERIOR INFERENTIAL STATISTICS OF THE EXPERIMENTAL DATA OF A COMPLEX EXPERIMENTAL CULTIVATOR

Abstract

The aim of the research is to highlight some statistical tools that favour extracting the components of the dynamic process that are dependent on the forward speed of some agricultural aggregates. The main objectives are: (I) identification of a minimum number of components in a multitude of random variables, with the help of which the other random variables can be calculated, and the application of this result to the strain gauge measurements; (II) establishing the connection between the synthetic results that partially solve the first objective and the forward speed of the agricultural aggregate. The second objective is used to obtain indications in search of the parameters’ dependencies on the forward speed of the aggregate. The first objective seeks to determine a group of three signals from the twelve, with the help of which the best multivariate linear interpolation is obtained for the other nine signals, which in physical terms means the reduction to a quarter of the measurement points and of the strain sensors used. A result associated with the first objective refers to the estimation of information loss due to the limited number of deformation sensors mounted on the tested structure. The article also presents attempts to use the results of the theory of neural networks and statistical interaction. In order to capitalise on the experimental data in this complex statistical framework, it is necessary to monitor at least the working speed (not only the average speed per experiment), fuel consumption, working depth (continuously monitored), soil moisture etc.