The research considers conducting orthogonal experiment (OT) as one of the stages in developing a new discrete element method (DEM) parameters calibration approach. The measured responses in experiment are the parameters obtained by DEM animation processing using machine vision system (MVS). The variable factors in experiment are DEM parameters. A brief overview of an existing calibration approaches given in the article. The choice of OT as a design of experiment tool among other mathematical tools discussed. Experiments conducted using specially developed rig where bulk material's flow captured as DEM animation. DEM animation converted to video and then processed using MVS that allow register the values of such parameters as angle of repose or expiration time (measured responses). The results of the OT show that it is possible to identify four measured responses with the most valuable correlation coefficient. DEM parameters with the biggest influence on the measured responses identified for each of the obtained regression. Obtained results are useful in learning or iterative algorithms development for DEM parameters calibration.

Qualification of employees who operate technological processes directly influences the safety of production. However, the employees’’ qualification cannot completely exclude human factor.
Today, there are many technologies that can minimize or eliminate human factor impact on production safety ensuring. The augmented reality technology is an example of this technology.
Nowadays, the augmented reality technologies and industrial technologies integration process moves to a new level of development. These technologies have huge experience, which has been accumulated in a long period of time. -This new level turns available by this experience combination and integration; it brings additional profit to the enterprise and can be a basis for completely new technologies. This paper shows an example of combination of augmented reality technology and oil pumps maintenance. For researching of efficiency of augmented reality system for oil pump maintenance, the laboratory unit with Grundfos vertical electric centrifugal pump (CR15-4 A-FGJ-AE-HQQE) was used. The laboratory unit is a physical model of one of the continuous oil processes. The oil pump of this laboratory unit is object of this research. The algorithm of servicing of oil pump was developed. The test of system and algorithms were carried out with four groups of people: the first one had only instructions to use on hand, the second one only used the internal recommendations of the system, the third one used only the help of an expert, and the fourth used internal recommendations and, if necessary, contacted the expert. The results show the efficiency and actuality of augmented reality technology for maintenance of industrial equipment, especially for the equipment operated in remote Arctic conditions.

Analysis of technical and technological conditions for the emergence of emergency situations during the operation of electromechanical equipment of enterprises of the mineral and raw materials complex shows that when developing the basis for ensuring safe operation, it is necessary to take into account not only the technical condition, but also the non-stationary operation of the operating conditions of equipment, and the nonstationarity of operational operating parameters of technological processes.
Violations of the operation of individual parts of the machine, not detected in time, can lead to severe accidents at work, as well as to unplanned downtime and loss of profits. That is why, the issues of obtaining and processing Big data obtained during the life cycle of electromechanical equipment, for assessing the current state of the electromechanical equipment used, timely diagnostics of emergency and pre-emergency modes of its operation, estimating the residual resource, as well as prediction the technical state on the basis of machine learning are very important.
This article is dedicated to developing the special method of data storing, collection and aggregation for definition of life-cycle resources of electromechanical equipment. This method can be used in working with big data and can allow extracting the knowledge from different data types: the plants' historical data and the factory historical data. The data of the plants contains the information about electromechanical equipment operation and the data of the factory contains the information about a production of electromechanical equipment.