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.

Improving energy efficiency is considered to be important and cost-effective strategies to reduce greenhouse gas emissions, which is crucial in the face of global climate change. This study hypothesizes that there are significant differences in the impact of energy efficiency on economic and energy systems. Using quantile regression method, the impact of economic and energy characteristics on energy-related greenhouse gas emissions in 120 countries over the period 2010–2020 is investigated. Detailed analyses are conducted by dividing the countries into 12 groups based on gross national income per capita (Atlas method) and the share of resource rents in gross domestic product. A macro-level system of indicators for assessing energy efficiency is proposed, which can be linked to the economic and energy characteristics of the systems: National Energy Efficiency, Energy Intensity Ratio, Primary Energy Production Efficiency, Industrial Sector Energy Efficiency, Share of Low Carbon Energy, Population Energy Efficiency. The findings show that (1) global energy efficiency has remained largely unchanged over the decade; (2) there are differences in energy efficiency between fossil fuel producing and non-fossil fuel producing countries; (3) the factors that have an inverse effect on emissions in fossil fuels dependent countries include industrial energy efficiency and population energy efficiency, which are far from exhausting their potential; (4) despite the recognized importance of energy efficiency, its impact on reducing greenhouse gas emissions globally has been unnoticeable. The study highlights that Sustainable Development Goals 7 and 13 on energy affordability and emission reductions are conflicting and require harmonization of mechanisms to achieve them, such as balancing climate change mitigation investments, identifying the potential for energy efficiency improvements across economy or improving energy saving practices. The overall conclusion suggests that there is an urgent need to review current decarbonization and energy efficiency initiatives, given the lack of significant global progress in reducing greenhouse gas emissions or improving energy efficiency between 2010 and 2020.

Destruction of rocks and artificial composites is carried out in various ways. One of the most common methods is the impact fracture. The conical pick of JCB HM380 hydraulic breaker was used as the impact tool. The experimental study of rock fracture was carried out on an artificial composite—concrete. The depth of penetration of the pick into concrete was used as the test parameter. The model was based on the model of Professor V. B. Sokolinsky. The model developed in this work takes into account the degree of bluntness of the tool insingle and multiple impact. The system was considered as a two-component system due to the high stiffness of the pick–granite contact (as against the hammer piston–pick stiffness). The wave processes in the pick were disregarded because of the small length of the pick. The pick penetration depth in rocks was calculated theoretically, using the developed mathematical model, at each impact of the pick, and then was compared with the experimental data. The fracture tests also used JCB HM380 breaker and concrete. The penetration depth was determined from the step-frame analysis of video filming of the process. It is found that the pick blunting causes a monotonic increase in the rock resistance to the pick penetration, both in single and multiple impact, while the depth of the tool penetration and the blow time decrease (as compared with the initial state of the tool, by 28% and 20%, respectively).