An Integrated Digital Twin Framework for Predictive Maintenance of Intelligent Logistics Equipment: A Stacker Crane Case Study

Predictive maintenance is essential for improving the reliability and service continuity of intelligent logistics equipment, particularly in remote or overseas operating environments. Existing digital twin‐based studies mainly focus on equipment monitoring and fault diagnosis, while less attention has been paid to how structural analysis, sensor deployment, and service‐support mechanisms can be integrated into a unified application framework. To address this gap, this study proposes a physics‐informed monitoring framework for predictive maintenance of intelligent logistics equipment, using a single‐column stacker crane as the representative case. Finite element analysis (FEA) identifies response‐sensitive regions, and the results are combined with failure mode and effects analysis to guide sensor deployment and monitoring priority definition. A digitalized operation and maintenance platform supports real‐time monitoring, fault warning, remote diagnosis, and maintenance decision‐making. AI‐assisted bidding and talent‐support modules are incorporated as upstream and downstream service components, presented as proof‐of‐concept prototypes. The case results demonstrate that the framework supports the transition from reactive to condition‐based and remote maintenance. Simulation‐derived performance indicators include a sensor response prediction accuracy of ~94.2% and a false alarm rate below 5.8% under the tested scenario. Scenario‐based estimation, derived from simulation configured with desensitized operational parameters reflecting real industrial conditions, suggests potential improvements in equipment availability and lifecycle maintenance cost; however, these estimates require further validation through long‐term field deployment data. The study extends predictive maintenance from an equipment‐monitoring method to an integrated service‐support framework, offering practical implications for sensor investment, remote maintenance management, and overseas workforce planning.