Predictive maintenance has become an important application of Artificial Intelligence in modern industries. Traditional maintenance techniques often lead to unexpected machine failures and increased operational costs. This research proposes an AI-based smart digital twin system that monitors machine performance and predicts possible failures before they occur. The digital twin model replicates the physical machine in a virtual environment using sensor data and machine learning algorithms. The system analyzes temperature, vibration, and operational parameters to detect abnormal patterns. Experimental results show that the proposed model can effectively identify potential faults and reduce downtime. This approach improves maintenance efficiency, increases equipment life, and reduces operational costs.
Sayyad et al. (Thu,) studied this question.