With the development of advanced data analysis technology, the manufacturing industry is moving towards the direction of intelligence. To solve the problems of low operation and maintenance (O&M) efficiency, passive O&M personnel and low intelligence of complex products, a digital twin-based intelligent O&M method for complex products is proposed. A digital twin intelligent O&M model with the physical O&M center, virtual O&M center, twin data platform and O&M service system is established. In the case of product fault classification, the maintenance mechanism is designed and the O&M process in different states is analyzed. Then the implementation process and key technologies are described in detail. Through the digital twin model, the virtual-real interaction and data dynamic update of O&M are realized. Finally, taking the key components of a certain type of EMU bogie as an example, the K-means clustering and Apriori algorithm are used to analyze the fault data. Moreover, the validity and feasibility of the proposed model are verified by applying the fault data to the digital twin architecture. The proposed model and key technologies can provide a new solution for the intelligent O&M of complex products.
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