An Industrial IoT Architecture for Autonomous Maintenance: Integrating Digital Twins, Distributed Control, and On-Demand Additive Manufacturing

Industrial Internet of Things (IIoT) technologies enable interconnected sensing, computation, and actuation across distributed industrial systems, but translating equipment health information into autonomous physical maintenance actions remains challenging. This work proposes and experimentally validates an IIoT-enabled Cyber-Physical Production System architecture that integrates Remaining Useful Life estimation, Digital Twin-based prescriptive logic, distributed control, and on-demand Additive Manufacturing to close the loop between failure prediction and maintenance execution. The architecture distributes sensing, prognostics, decision-making, and manufacturing functions across heterogeneous edge nodes using IEC 61499-compliant function blocks, DINASORE, and MQTT-based communication. A nine-node physical testbed comprising degrading robotic systems and a 3D printer was developed to evaluate individual components and the complete information-to-action workflow. Experimental results demonstrate reliable distributed data acquisition and control, online degradation assessment, and autonomous triggering of replacement-part production. During closed-loop validation, degradation of a previously unseen component caused its predicted health state to cross the prescribed threshold and automatically initiated fabrication, with a mean orchestration latency of approximately 225ms and no human intervention in the decision path. The results demonstrate how IIoT connectivity, distributed intelligence, and cyber-physical actuation can support autonomous maintenance workflows linking equipment health monitoring directly to physical manufacturing resources.