This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN‐classifier). Experiments are carried out in la-boratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN‐classifier. A method of providing the data into the classi-fier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.

The research is dedicated to the development of special devices (capsules) that can be used to control the mining ore behavior in the technological unit in order to increase processes efficiency. In the first part of the article, the choice of the discrete element method for gen-erating various particle trajectories in the unit (drum pelletizer) was substantiated. This part describes the specific technologies that were used to recognize the pelletizing mode. In par-ticular, conversation of paths to sensor readings is implemented using the Matlab Sensor Fusion and Tracking Toolbox. The obtained readings were processed using two neural network classifiers (DNN and LSTM). As a result, stable models for recognizing the pelletizing modes of the unit were obtained. LSTM recognition accuracy is greater than DNN. The developed approach can be used to recognize the operating modes of other technological units. In addition, data on particles trajectories can be used to improve DEM models of technological processes. Future work consists of the capsule physical implementation and testing the recognition algorithm on a real unit.

The article presents a study of implementations of information technologies for energy saving. The study provides an analysis of cloud platforms used in the energy sector. Most platforms are used to increase equipment load factors, diagnose it, or integrate the energy infrastructure of individual systems into large systems. The article discusses the role of man in the process of improving energy efficiency. As a result of the research, it is found that the user's connection to energy saving will act as a significant motivation. The proposed cloud platform focused on the personalization of energy actions will create the prerequisites for the formation of a smart consumer. The article describes the structure and functionality of the system. As a result of the platform implementation, the user will be able not only to reduce his bills for energy, but to see the real effect of his actions in the overall balance.