Framework for the Application of Digital Twin Technology in Intelligent Production Line Condition Monitoring and Predictive Maintenance
DOI:
https://doi.org/10.71451/ISTAER2572Keywords:
Digital twin; Intelligent production line; Condition monitoring; Predictive maintenance; Framework constructionAbstract
This paper addresses the urgent needs of intelligent manufacturing for highly reliable equipment operation and precise maintenance and delves into the innovative application of digital twin technology in production line condition monitoring and predictive maintenance. By systematically reviewing the core theories of digital twins, multi-source heterogeneous data acquisition and processing technologies, and predictive maintenance methodologies, a five-dimensional integrated framework comprising a physical layer, data layer, model layer, functional layer, and application layer is constructed. This framework innovatively achieves real-time dynamic mapping and bidirectional interaction between physical and virtual spaces, establishes a data-model hybrid-driven mechanism for equipment health status assessment and remaining life prediction, and forms a closed-loop optimization system from condition perception and fault early warning to maintenance decision-making. To verify the effectiveness of the framework, this study conducts a case study using a precision CNC gear machining production line. The results show that the framework can control the latency of critical equipment condition monitoring within 200 milliseconds, improve the accuracy of remaining life prediction by approximately 15% compared to purely data-driven methods, successfully achieve early fault warning, reduce unplanned downtime by 65%, and save maintenance costs by 28%. The research findings provide theoretical guidance and practical examples for achieving precise and forward-looking equipment health management in the context of intelligent manufacturing and have important reference value for promoting the digital transformation of the manufacturing industry.
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