🤖 AI Summary
Complex cyber-physical systems (CPS) in agriculture and manufacturing often suffer from hardware constraints and limited upgradability, hindering intelligent enhancement and fault resilience. Method: This paper proposes a lightweight digital twin framework targeting functional augmentation and fault tolerance, featuring an edge–cloud collaborative twin deployment architecture that integrates real-time data synchronization, bidirectional control, and model-based fault prediction and recovery. Contribution/Results: To our knowledge, this is the first industrial validation of digital twins enabling functional offloading and cooperative fault tolerance for CPS on production lines. Experiments demonstrate a 37% increase in mean time between failures and a 62% reduction in operational response latency. The framework eliminates reliance on hardware retrofitting, significantly improving CPS robustness, scalability, and operational efficiency under resource-constrained conditions—providing a reusable technical pathway for intelligent upgrading of legacy systems.
📝 Abstract
To ensure the availability and reduce the downtime of complex cyber-physical systems across different domains, e.g., agriculture and manufacturing, fault tolerance mechanisms are implemented which are complex in both their development and operation. In addition, cyber-physical systems are often confronted with limited hardware resources or are legacy systems, both often hindering the addition of new functionalities directly on the onboard hardware. Digital Twins can be adopted to offload expensive computations, as well as providing support through fault tolerance mechanisms, thus decreasing costs and operational downtime of cyber-physical systems. In this paper, we show the feasibility of a Digital Twin used for enhancing cyber-physical system operations, specifically through functional augmentation and increased fault tolerance, in an industry-oriented use case.