A Digital Twin-Driven Deep Learning Framework for Real-Time Fault Prediction and Predictive Maintenance in Smart Manufacturing

This manuscript presents a proposed framework integrating Digital Twin technology with deep learning for predictive maintenance in smart manufacturing. The framework synchronizes IoT sensor data with a virtual representation of industrial equipment to predict faults, estimate remaining useful life, and support maintenance decisions. This draft is intended as a research manuscript template; experimental validation and results should be added after implementation.

Digital Twin, Predictive Maintenance, Deep Learning, Industry 5.0, IoT

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