The article examines methods for assessing the efficiency of electric mining excavators, emphasizing the inseparability of operational efficiency from the operator-machine ergatic system. It reviews methods for evaluating operator skills via experimental data and proposes a comprehensive approach to assess the excavator's operational efficiency and the operator's skill level. This method includes analyzing the machine's operating time and energy efficiency using a simulator, thereby offering a novel perspective on the dynamic interaction between human operators and automated systems. With the working cycle's duration measured by the ratio of average to nominal cycle times, and energy efficiency assessed through the comparison of specific energy consumption to theoretical values. The findings suggest prioritizing reductions in operating cycle time for suboptimal machine control and focusing on improving bucket fill rates to enhance energy efficiency. Moreover, the study underscores the potential for utilizing these methodologies in real-world applications, aiming to optimize the utilization of mining equipment and thereby significantly contribute to the advancement of operational methodologies in the mining sector.

In the era of digital technology, more and more of it finds application in various industries. This paper proposes to use technology of augmented reality for maintenance of stirred tank reactors. The proposed approach can be applied to any type of equipment, as it can be easily integrated with the existing automation systems and does not require much investment at the initial stage, implying gradual optimization and functionality build-up. This paper describes the basic set of functional requirements of an augmented reality-based maintenance system, methods of assessing the system performance, as well as scaling-up and streamlining prospects. The paper also describes how such systems can be integrated with existing control systems of a production company. The effectiveness of the developed augmented reality-based system was verified by determining the average execution time of each service stage and processing the outcomes using the Mann-Whitney U test. The use of the augmented reality system resulted in the reduction of the average service time by 2.3 times, while the maintenance efficiency increased by 5%.

This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.