The paper presents a methodology for training neural networks for vision tasks on synthe-sized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.

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.

The electric drive is strategically placed in the power industry. It is exposed to wear and tear, defects, and constructional damage, as is any technical device. An information–analytical system is presented in this work. It performs the tasks of monitoring, diagnostics, general assessment of technical condition, and continuous assessment of energy and mechanical efficiency of the electric drive based on the analysis of immediate values of currents and voltages. The system modules are finished products with practical application, which are supported by experimental validation. This article contains a detailed description of the methods implemented in the system development, as well as a description of the laboratory bench and equipment used in our experiments. The information–analytical system is shown and proved on the basis of a fault reconstruction example with electric drive misalignment. According to the obtained results, recommendations for preventive control and proposals for development in this direction are formulated.