Development of a Digital Twin-Based Intelligent Monitoring System for Industrial Machinery Using Edge Computing and Machine Learning in Industry 4.0 Environments
The rapid advancement of Industry 4.0 has accelerated the adoption of intelligent monitoring systems for
industrial machinery, requiring efficient solutions for real-time data processing and predictive analytics.
Traditional monitoring approaches are often limited by latency, lack of scalability, and insufficient
analytical capabilities. This study proposes a Digital Twin-based intelligent monitoring framework that
integrates IoT sensor data, edge computing, and machine learning techniques to enable real-time system
analysis and fault detection. The Digital Twin continuously synchronizes with physical machinery,
providing a dynamic virtual representation for monitoring and decision-making. Edge computing is
employed to process data locally, significantly reducing latency and enhancing system responsiveness. A
machine learning-based model is incorporated to analyze sensor data and detect anomalies, improving
predictive accuracy. Experimental results demonstrate that the proposed system achieves high accuracy
with reduced latency and improved anomaly detection performance, making it suitable for real-time
industrial applications. The integration of Digital Twin technology with edge computing and machine
learning provides a scalable and efficient solution for intelligent monitoring in smart manufacturing
environments.
