Construction method and application of digital twin model for bridge crane

As the core equipment for industrial material lifting, the safe operation of bridge cranes is essential to ensuring production efficiency. A five-dimensional digital twin model framework is proposed for the full lifecycle management of bridge cranes to address the lack of real-time performance and lifespan prediction in traditional detection methods that rely on manual inspection and static evaluation. The digital twin model supports real-time mapping and dynamic correction of the crane's operating status, established through multidimensional fusion of the physical entity layer, virtual model layer, twin data layer, connection layer, and service function layer. And analyzed that the model involves the key technology: physical virtual model synchronous operation and interaction technology. Analyzed the application of predicting the remaining life of key components, based on the digital twin model combined with convolutional neural network (CNN) and gated neural network (GRU), providing theoretical support and engineering practice reference for bridge crane’s intelligent operation and safety control.