Development of a Machine Learning Model for Production Facility Predictive Diagnostics

The usage of artificial intelligence in problems related to industry makes it possible to develop optimization, predictive digital models for their operation in real time. The developed model can conduct predictive diagnostics of equipment's, or can be employed as a system that offers recommendations to the user for subsequent optimization of the technological process Considering the growing complexity of technical equipment and their control systems, it becomes highly imperative to develop an intelligent diagnostic structure for safety management in multi-level industrial systems. The article's relevance stems from the implementation of machine learning algorithms in the industry, enabling the analysis of a considerable amount of sensor-derived data. As a result, this facilitates predictive equipment diagnostics, anomaly prediction, intelligent safety management, uninterrupted operations, and reduced maintenance downtime.