Abstract
This study aims to develop a digital twin model based on Bayesian methods for accurate and adaptive failure prediction in complex engineering systems operating under uncertainty. To achieve this, the research employs Bayesian networks that model probabilistic relationships among sensor data such as temperature, pressure, vibration, and humidity enabling real-time updates and robust predictions. The methodology involves training and testing the model on real-world industrial datasets, where it demonstrated superior performance. Experimental results revealed that the proposed model achieved an accuracy of 91%, which is 16% higher than traditional approaches, and also outperformed others in terms of F1-score and ROC-AUC. These findings highlight the strength of Bayesian inference in handling incomplete or noisy data and maintaining high interpretability. The study concludes that integrating Bayesian models into digital twins enhances their adaptability and reliability for critical systems. In practical terms, the developed approach can significantly improve predictive maintenance, reduce operational risks, and support real-time decision-making in industrial environments. Furthermore, the integration of this solution with Internet of Things (IoT) and Industrial IoT (IIoT) technologies is identified as a promising direction for future research, aimed at creating intelligent, self-updating monitoring systems.