This paper presents a model for determining fuzzy evaluations of partial indicators of the availability of continuous systems at coal open pits using a neuro-fuzzy inference system. The system itself is a combination of fuzzy logic and artificial neural networks. The system availability is divided into partial indicators. By combining the fuzzy logic and artificial neural networks, a model is obtained that has the ability to learn and uses expert judgment for that learning. This paper deals with the ECC system (bucket wheel excavator-conveyor-crushing plant) of the open pit Drmno-Kostolac, which operates within the Electric Power Company of Serbia. The advantage of a model of this type is that it does not rely on the historical experiences of experts and usual predicted values for the fuzzy evaluation of partial indicators, which are based on the assumption that similar systems affect availability in a similar way. The fuzzy evaluation of partial indicators is based on historical data for the specific system for which the model was created. As such, it can more accurately predict continuous systems availability on the basis of expert evaluations in the appropriate time period. Another advantage of this model is that the availability is estimated on a quarterly basis, which gives a more accurate view because it uses a smaller time period with more similar characteristics and, thus, includes certain external influences which are related to the quarterly meteorological conditions.

The bucket-wheel dredge “Kovin I” for underwater coal mining with bucket-wheel type UCW-450 has been in operation for over 20 years. Based on analyzing the bucket-wheel dredger performance, productivity, maintenance costs, and reliability, a rational decision was made: to rehabilitate the most essential parts of the dredge, including the bucket wheel and the gearbox. However, the selection and construction of the excavator parts were performed on the ground of available laboratory data for digging resistance. The data itself was determined by the testing methodology that did not include the influence of surrounding water pressure at a certain depth of mining. According to the previous findings, it was necessary to develop a specific research and testing program that would involve appropriate laboratory testing of the geomechanical parameters. These were to represent the influence of hydrostatic water pressure on the working environment—coal. Nevertheless, geomechanical laboratory research tests were initially modified to provide reliable data of cutting resistance, especially in the water under different hydrostatic pressures, fully simulating the “in situ” working conditions of mining, i.e., cutting.

Even in the third decade of the 21st century, large power plants are still responsible for more than half of the electricity production in Europe. They predominantly use fossil fuels in their work, which results in the emission of pollutants into the air. Nevertheless, thanks to EU directives, as well as disturbances on the market, emissions of carbon dioxide from large combustion plants in the EU-27 are intensively decreasing in the last two decades. Although the use of coal, especially in the countries of the European Union, is in constant decline, there is space for additional reduction. Through this paper, the authors wanted to present the completely actual situation of emissions of CO2 from thermal power plants in Europe, that have used coal for electricity production, as well as to compare such plants with those operating in the Republic of Serbia. The CO2 emission was analysed in relation to the installed capacity, as well as compared to the energy produced and supplied to the system. By analysing the obtained values of the ratio of emissions to the number of inhabitants, the area of the territory, production and consumption of electricity, as well as to the GDP, it is noticeable that a much larger amount of CO2 is generated on the territory of the Republic of Serbia to achieve a similar energy, industrial and economic effect compared to the EU-27. By researching location data of coal burning in the production of electricity in the Republic of Serbia revealed the existence of a large difference in the amount of CO2 emissions, which is a consequence of the composition of the coal used and the combustion modes in thermal power plants.

Industry 4.0 marks the fourth industrial revolution, characterized by the use of cyber-physical systems. In order to achieve an optimal maintenance strategy (but also production), it is necessary to develop systems that support advanced intelligent maintenance systems or smart maintenance technologies. This resulted in the postulates of Predictive Maintenance 4.0, which define the very near future in the field of maintenance of technical systems. Predictive Maintenance 4.0 involves harnessing the power of artificial intelligence to create ongoing insights into detecting causes and anomalies in equipment operations that are not detected by cognitive power. In other words, Predictive Maintenance 4.0 makes it possible to predict what was previously unpredictable. Industry 5.0 focuses on the return of human hands and minds to the industrial framework. The man and machine harmonize with each other and find ways to work together to improve production / maintenance efficiency.

The drive of the bucket-wheel on SRs1200 excavators is realized by a 400 kW electric motor and a multi-stage gearbox through which power and torque are transmitted from the drive motor to the bucket-wheel. The gearboxes used on these excavators are of a conventional extended design with parallel shafts and pairs of helical cylindrical gears, equipped with a main and an auxiliary drive. The main drive is used during bucket wheel operation, while the auxiliary drive is applied during overhaul activities and inspection. From the input shaft of the main drive to the output shaft, a four-stage gear transmission is formed. In previous designs, the gear on the output shaft was manufactured by casting, while the gearbox output shaft is hollow, allowing the bucket wheel shaft to be mounted through it. The objective of the research is the implementation of two different methods, one theoretical and one practical, for diagnosing the behavior and vibrations occurring in the drive group, with the aim of determining the most optimal approach to operation, maintenance, and necessary reconstruction of the gearbox. The basic diagnostic parameters are vibration values measured at characteristic locations throughout the drive group and its supporting structure. These measurements show good agreement with a mathematical 3D model developed using the Inventor software package, based on the finite element method, the theory of elasticity, and machine dynamics. Testing was performed prior to installation, followed by inspection after a certain number of operating hours, reconstruction of the gear teeth, and testing after reconstruction. A reduction in drive group vibrations of approximately 30% was achieved. The scientific contribution lies in the potential for future development of gearbox condition analysis models based on measured vibration parameters.

The paper presents an integral procedure for conducting experimental measurements on excavation machines. Excavators have a complex structure with pronounced dynamic behavior. The identification of exploitation behavior is observed through experimental measurement of stress and acceleration, drive load, and vibrations. Electro-resistive measuring tapes were used to observe the steel structure, devices for measuring current, i.e. engaged power on the drives, as well as devices for measuring vibrations at characteristic points of the drive. The results obtained realistically reflect the condition and behavior of the structure and drive equipment. The goal is to introduce systematic research to monitor the condition and behavior of the equipment on the excavator. This approach forms the backbone of predictive observation, influencing the proper management of the excavator. Experimental measurements are performed to prove the correctness of the numerical model and to diagnose the condition and behavior of the structure and power units. By monitoring the condition and behavior of the equipment, we can optimally influence the process of maintenance of the equipment as well as the lifespan of the mining machine. This work includes the most important experimental measurements to carry out reconstructions, revitalizations, and modernizations on mining machines.