This article is devoted to expanding the possibilities of assessing the technical state of the current consumption of asynchronous electric drives, as well as increasing the information capacity of diagnostic methods, in conditions of limited access to equipment and incompleteness of information. The method of spectral analysis of the electric drive current can be supplemented by an analysis of the components of the current of the Park's vector. The research of the hodograph evolution in the moment of appearance and development of defects was carried out using the example of current asymmetry in the phases of an induction motor. The result of the study is the new diagnostic parameters of the asynchronous electric drive. During the research, it was proved that the proposed diagnostic parameters allow determining the type and level of the defect. At the same time, there is no need to stop the equipment and taky it out of service for repair. Modern digital control and monitoring systems can use the proposed parameters based on the stator current of an electrical machine to improve the accuracy and reliability of obtaining diagnostic patterns and predicting their changes in order to improve the equipment maintenance systems. This approach can also be used in systems and objects where there are significant parasitic vibrations and unsteady loads. The extraction of useful information can be carried out in electric drive systems in the structure of which there is a power electric converter.

The aim of this paper is to study the numerical modeling of the crushing process in a jaw crusher with complex motion jaws using Discrete Element Method (DEM). The main goals are to ensure the conformity of numerical simulation results to the experimental data, optimize the operating modes and the crusher design and develop automated verification and calibration methodology. It is offered to use automated selection of mathematical model parameters using large- or laboratory-scale experiments instead of deriving parameters from conventional intensive material tests (e.g. drop-weight test). Experimental data were obtained from a large-scale crusher designed and manufactured by the company “Mekhanobr-Tekhnika”. Both jaws have their drive shafts located asymmetrically; one jaw is driven in the upper part, the second at the base. The crushing process involves fragmentation of single smoothed rectangular granite particles of the same weight. During the experiment, the required parameters such as particle size distribution were obtained for further computer simulations. Based on the obtained experimental data, the Rocky DEM model was built. Simulation breakage parameters were calibrated using automated parameter selection methodology based on multiple set of parameter-controlled simulations. A good correlation of the experimental data and numerical simulation data was obtained. The methodology in combination with well-known capabilities of simulation modeling will help reduce the research time in the development of new mineral processing equipment.

This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN‐classifier). Experiments are carried out in la-boratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN‐classifier. A method of providing the data into the classi-fier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.