| Title | Digital Twin-Driven Predictive Maintenance Using Artificial Intelligence for Smart Manufacturing Systems |
| Research Area | Artificial Intelligence |
| Abstract | The transition toward Industry 4.0 has transformed manufacturing systems into highly connected,
data-intensive, and increasingly autonomous environments. Industrial machines continuously
generate information through sensors, controllers, production systems, and monitoring devices. This
information provides an opportunity to move beyond traditional reactive and preventive maintenance
toward predictive and prescriptive maintenance strategies. Digital Twin technology provides a
particularly promising foundation because it enables a virtual representation of a physical asset to be
continuously updated using operational data. When Digital Twins are combined with Artificial
Intelligence and Machine Learning, they can support condition monitoring, anomaly detection, fault
diagnosis, Remaining Useful Life estimation, maintenance scheduling, and operational decision
making.
This paper presents a conceptual Digital Twin-driven predictive maintenance framework for smart
manufacturing systems. The proposed architecture integrates physical equipment, Industrial Internet
of Things sensors, edge computing, digital representations, artificial intelligence models,
maintenance decision services, and human operators. The study examines the role of historical and
real-time data, data preprocessing, feature engineering, machine learning, deep learning, anomaly
detection, Remaining Useful Life prediction, uncertainty management, explainability, and
maintenance optimization. Particular attention is given to the practical challenges of Digital Twin
deployment, including data quality, model synchronization, interoperability, computational
requirements, cybersecurity, model drift, limited failure data, and organizational readiness. |
| Keywords | Digital Twin, Predictive Maintenance, Artificial Intelligence, Machine Learning, Industry 4.0, Smart Manufacturing, Industrial IoT, Remaining Useful Life, Edge Computing, Fault Diagnosis |
| Paper Status | Published |
| Volume | 2 |
| Issue | 5 |
| Published On | 15/09/2026 |
| Published File |
IJSRTD_4894.pdf
|