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.

Big Data analysis is becoming an everyday task for companies all over the world, including Russian companies. Due to advances in technology and the reduction in the cost of storage systems, companies can now collect and store large volumes of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will eventually be met by all companies seeking to maintain their competitiveness and place in the market. However, companies face several challenges when it comes to collecting, pre-processing, and integrating data into cohesive data sets designed to deliver analytics. In this article, the above problems and possible solutions are illustrated using the example of cleaning, integration, and normalization of data obtained in the measurement of indicators for the Vanyukov melting furnace process. The article considers an approach to the study of metallurgical processes using the analysis of large operational control data sets. Standard methods of processing the data sets of operational process control are used. The correlation analysis of the main process parameters is carried out. The results are interpreted for their further practical application.

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.