Digital Twin Models for Real-Time Failure Prediction in Industrial Machinery

Digital twin models have emerged as transformative tools in industrial maintenance, enabling real-time failure prediction and reducing unexpected downtime. This study investigates the application of digital twin technology for proactive failure prediction in industrial machinery, utilising a combination of sensor data, advanced simulations, and machine learning algorithms. By creating a virtual replica of machinery components, the digital twin continuously monitors operational parameters such as vibration, temperature, and pressure, allowing for real-time assessment of equipment health. The model integrates historical data to enhance predictive accuracy, dynamically updating failure forecasts and alerting operators to potential issues before they escalate. Results indicate that digital twin models significantly improve prediction precision compared to traditional methods, enabling more effective and timely maintenance interventions. The implementation of digital twin models presents a promising avenue for optimising machinery lifespan, reducing maintenance costs, and enhancing operational efficiency within industrial environments.