Digital Twin-Driven Predictive Maintenance Framework for Complex Mechanical Systems in Industry 4.0

Hao Li

doi:10.71222/a33ypx94

Authors

Hao Li University of Illinois at Chicago, Chicago, USA Author

DOI:

https://doi.org/10.71222/a33ypx94

Keywords:

Digital Twin, Predictive Maintenance, Manufacture 4.0, Mechanical Systems, Operational Efficiency

Abstract

This research article intrinsically salute a Digital Twin-Driven Predictive Maintenance Framework orient for scheme within the Industry 4.0 prototype. The study explores the integration of digital twins with advanced predictive analytics to enhance system reliability and operational efficiency. A taxonomical methodology is proposed, comprehend data acquisition. Genuine-time simulation. And prognosticative moulding. Experimental termination demonstrate significant improvement in fault detection accuracy and maintenance scheduling efficiency. The discourse intrinsically highlights the model's scalability, adaptability. And likely challenge in industrial execution. This work contributes to encourage prognosticative maintenance strategies, aligning with the goal of Industry 4.0 to optimize resource utilization and understate downtime.

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