This article describes an approach of mathematical processing of signals (seismograms) from five blasthole charges from experimental blasting, each 3 m deep, with equal explosive weight (1 kg), and equidistant (3 m) from one other. The seismic explosive waves were measured at a 13 to 25 m distance. This article provides spectral analysis, wavelet analysis, and fractal analysis results. It defines the dependence of dominant frequency and amplitude on the distance to the blast center. According to the experimental data, the dominant frequency is calculated as y = 1.0262x^0.2622 and the amplitude dependency as y = 18.139x^−2.276. Furthermore, the analysis shows that 80% of the entire signal is concentrated in half the area of frequency range, i.e., the low frequency zone is of the most interest. This research defines the dependence of distance on the energy value of signal wavelet analysis. It is demonstrated that, according to the experimental data, the 12th frequency range is closely correlated with the distance values. This article gives the definitions of entropy, correlation dimension, and predictability time. This experiment shows that entropy and correlation dimension decrease but predictability time increases when the distance to the blast center increases. This article also describes the method for determining optimal drilling and blasting parameters, and concludes with the possibility of applying the analytical results to predicting and enhancing drilling and blasting operations.

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 relevance of the research is due to the need to stabilize the composition of the melting products of copper-nickel sulfide raw materials in the Vanyukov furnace. The goal of this research is to identify the most suitable methods for the aggregation of the real time data for the development of a mathematical model for control of the technological process of melting copper-nickel sulfide raw materials in the Vanyukov furnace. Statistical methods of analyzing the historical data of the real technological object and the correlation analysis of process parameters are described. Factors that exert the greatest influence on the main output parameter (copper content in matte) and ensure the physical-chemical transformations are revealed. An approach to the processing of the real time data for the development of a mathematical model for control of the melting process is proposed. The stages of processing the real time information are considered. The adopted methodology for the aggregation of data suitable for the development of a control model for the technological process of melting copper-nickel sulfide raw materials in the Vanyukov furnace allows us to interpret the obtained results for their further practical application.

The frequency of maintenance of electromechanical equipment depends on the plant, which uses and runs this equipment. Very often the maintenance frequency is poorly correlated with the actual state of the electromechanical equipment. Furthermore, traditional methods of diagnosis sometimes cannot work without stopping the process (for example, for equipment located in hard to reach places) and so the maintenance costs are increased. This problem can be solved using the indirect methods of diagnosing of the electromechanical equipment. The indirect methods often use the parameters in the real time and seldom use the parameters of traditional diagnostic methods for determination of the resource of electromechanical equipment. This article is dedicated to developing the structure of a special automated control system. This system must use the big flow of the information about the direct and indirect parameters of the equipment state from plants from different areas of industry and factories which produce the electromechanical equipment.

Any industrial enterprise serves a significant fleet of electrical equipment. Equipment servicing takes place in various ways: according to the schedule and without it, by direct and indirect methods, in manual and automated mode, with or without the involvement of specialists, etc. Timely maintenance prolongs the life of electrical equipment. So technical maintenance is indirectly related to the efficiency of production. Today there are many different technologies that simplify the process of maintenance of electrical equipment. One of these technologies is augmented reality technology. The existing practice of application of augmented reality technology shows that the one of the main difficulty faced by the enterprise is the lack of unified solutions and software modules. As a rule, the electrical equipment maintenance system is developed specifically for each type of equipment; its software modules and components are unique. This article describes the approach of developing a unified module of augmented reality technology for servicing electrical equipment.

The various devices based on vibration, thermal, electromagnetic and other methods are used to diagnose an asynchronous motor today. The use of vibration diagnostics is common for large machines. The emergency stop of the large machines leads to significant downtime and non-produced products meaning that the enterprise incurs significant material losses. However, for low-power engines, vibration diagnostics are used only as a periodic assessment of the state without accumulation of data and their subsequent analysis. It should also be noted that the vibration signals measured from the rolling bearing are affected by varying operating conditions and noise that distort the signal and complicate the faults identification for further evaluation of the residual life. In a number of cases, such as submersible pumps, nuclear power plant safety aggregates, mining equipment, it is practically impossible to use vibration, thermal and other methods for diagnosing asynchronous motors. Therefore, the actual task of this research is the development of a method for diagnosing and estimating the residual life in conditions of the systems without any maintenance, based on the electric motor energy parameters.