Abstract
In modern industrial settings, one of the primary challenges is ensuring uninterrupted equipment operation while minimizing production downtime. The aim of this research was to develop a digital twin capable of real-time assessment of equipment failure probability based on incoming sensor data. A Bayesian approach was employed as the methodological foundation: the model sequentially updates the prior probability of failure using new data obtained from sensors. The prototype was implemented in the R programming language, which enabled effective visualization of dynamic changes in failure probability. A simulation of sensor data was conducted to demonstrate how the posterior probability of equipment failure evolves at each stage. The model exhibited adaptability to changing operational conditions while maintaining high accuracy in risk assessment. The developed digital twin has proven effective as a tool for assessing the technical condition of the equipment. The use of a Bayesian framework ensures model flexibility and facilitates its integration into monitoring systems for predictive analytics. The proposed solution offers substantial practical value for enterprises pursuing the digitalization of production processes and the implementation of intelligent maintenance systems. The developed prototype can be integrated into existing monitoring infrastructure, enabling timely detection of failure indicators, reducing repair costs, and improving equipment reliability.