The emergence of abnormal events (e.g. personnel abnormalities, equipment failures, etc.) on the assembly floor of complex products can seriously affect normal assembly progress. In response to the problems of poor timeliness and lack of predictability in the control of abnormal events on the assembly floor, a method for predicting abnormal events on the assembly floor of complex products based on digital twin technology is proposed. A model for predicting abnormal assembly events is constructed with the physical assembly workshop, virtual assembly workshop, assembly workshop twin data platform and abnormal events prediction service system working together, and its prediction operation mechanism is designed based on the classification of abnormal events and the workflow of the mechanism under different states is analysed. The Grey-Markov method is used to predict abnormal assembly events and provide real-time information for the planning and scheduling system. In order to verify the effectiveness of this scheme, combined with the electrical multiple units bogie assembly workshop, the prediction of the number of equipment failures at the bottleneck station is achieved. The prediction accuracy is much better than that of the GM(1,1) model and can be applied to actual production.

This paper presents a complex product design-manufacturing-operations and maintenance integration method based on digital twin technology. This method aims to solve the problem of information silos in the design, manufacturing and operation and maintenance phases of complex products in the context of intelligent manufacturing and to integrate the design, manufacturing and operation and maintenance processes of complex products. To address the integration needs of complex product design, manufacturing, operation and maintenance business integration, a framework for complex product design-manufacturing-operation and maintenance integration based on the digital twin is first proposed, in addition to designing a model and operation mechanism for combining the virtual and real of the digital twin model. Then, the implementation of multistage collaborative design technology, data intelligent sensing technology, and data integration and fusion technology for the digital twin-based design-manufacturing-operations and maintenance integration processes are analyzed and discussed. Finally, a case study involving the fault prediction of key components of the bogie of an EMU demonstrated the integrated mode of operation in the design-manufacture-operation and maintenance process of the EMU. It verified the effectiveness of the proposed framework, process and methodology.