Concept of a Digital Twin of Metalworking Equipment Using Machine Learning Methods in Vibration Diagnostics

A concept of an intelligent vibration diagnostics system for technological equipment is developed based on a digital twin architecture, employing machine learning methods and integrated with Russian and international standards. This system enables a comprehensive assessment of the technical condition of equipment based on multiparametric data and residual life prediction using machine learning and degradation trend analysis.