A lot of researchers are developing new DEM parameters calibration approaches based on an experiment plan or the use of learning algorithms. This research is aimed at improving iterative algorithms frequently used for calibration. The big time consumption as a main problem of iterative algorithms is questioned. It is proposed to use Random forest algorithm to determine DEM parameters impact on the measured bulk responses. Measured responses are the parameters obtained by image processing using a technical vision system. As a result of 200 experiments processing, DEM parameters impact values on each bulk response were generated and presented as histograms. Obtained results were interpreted on the basis of the bulk material behavior and its physical properties. There is a discussion on the possibility of developing a universal DEM parameters calibration method based on the iterative algorithm.

In this article actual topics concerned with mechanical properties of bulk materials, usage of computer vision and artificial neural networks in this research are discussed. The main principles of the system for analysis of bulk materials mechanical characteristics are described. Bulk material outflow behaviour with predefined parameters (particles shapes and radius, coefficients of friction, etc.) was modelled. The outflow was modelled from the calibrated conical funnel. Obtained dependencies between mechanical characteristics and pile geometrical properties are represented as diagrams and graphs.

The presented research is focused on two issues. The first one is finding additional parameters that characterize the technical condition of an electric machine according to the current consumption. The second one is increasing of the informativeness of diagnostic methods in conditions of limited access to equipment and incomplete information. The paper discusses a method for diagnosing low and medium power asynchronous electric motors by electrical parameters. It is shown that asynchronous electric motors powered from a self-commutated voltage inverter have contained higher harmonic components in addition to the main harmonic. The non-sinusoidal voltage leads to the emerging of the highest current components in the stator winding of the motor. This is the reason for occurring of a pulsating component of the torque on the motor shaft. Therefore, there are two types of ripple: the one caused by the power supply from the self-commutated inverter and the one caused by defects. The moments’ coincidence of these ripples leads to increasing in the amplitude of oscillations. However, significant noise due to the presence of an autonomous inverter does not allow using the current spectrum to diagnose damage. Analysis of the hodographs of the Park’s vector of the stator current and voltage allows one to exclude harmonic components that were formed by the carrier frequency (and multiple of it). At the same time, analyzing the modulus of the Park’s vector and its spectrum, one can note pulsations of the amplitude values of the vector modulus corresponding to certain damage.