2518-1092Research result. Information technologies2518-109210.18413/2518-1092-2024-9-3-0-23556INFORMATION SYSTEM AND TECHNOLOGIES<strong>THE USE OF PREDICTIVE ANALYTICS TOOLS TO PREDICT THE RECOVERY OF LARGE-SIZED EQUIPMENT</strong><strong>THE USE OF PREDICTIVE ANALYTICS TOOLS TO PREDICT THE RECOVERY OF LARGE-SIZED EQUIPMENT</strong>BondarenkoJulia AnatolyevnaBondarenkoJulia AnatolyevnaYavurikOlga VasilyevnaYavurikOlga Vasilyevnayavurik@bsu.edu.ruLomakinVladimir VasilyevichLomakinVladimir Vasilyevichlomakin@bsu.edu.ru20249300

The article presents the results of a study in the field of predictive analytics for the repair of large-sized equipment in the building materials industry. New data processing techniques and the use of artificial intelligence, especially machine learning, can improve the prediction of the future state of equipment. The stages of predictive analytics application are presented, the possibility of predicting the residual resource is described and the effective use of predictive analytics is demonstrated using the example of restoring the trunnion of a ball mill. The data obtained are analyzed, on the basis of which it is concluded that repair work is necessary to increase the inter-repair life of the equipment and reduce economic losses. In conclusion, the validity of the use of predictive analytics for the repair of large-sized equipment and making optimal decisions is emphasized. The use of predictive analytics makes it possible to increase the efficiency of repair of large-sized equipment by increasing the repair life, reducing the number of downtime and preventing unplanned breakdowns. This approach is a promising and practical solution for enterprises in the building materials industry.

The article presents the results of a study in the field of predictive analytics for the repair of large-sized equipment in the building materials industry. New data processing techniques and the use of artificial intelligence, especially machine learning, can improve the prediction of the future state of equipment. The stages of predictive analytics application are presented, the possibility of predicting the residual resource is described and the effective use of predictive analytics is demonstrated using the example of restoring the trunnion of a ball mill. The data obtained are analyzed, on the basis of which it is concluded that repair work is necessary to increase the inter-repair life of the equipment and reduce economic losses. In conclusion, the validity of the use of predictive analytics for the repair of large-sized equipment and making optimal decisions is emphasized. The use of predictive analytics makes it possible to increase the efficiency of repair of large-sized equipment by increasing the repair life, reducing the number of downtime and preventing unplanned breakdowns. This approach is a promising and practical solution for enterprises in the building materials industry.

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