Research on abnormal event diagnosis method of complex product production based on digital twin

Abstract

Facing the challenges of production management of manufacturing enterprises and the demand for intelligent control of manufacturing system abnormalities, this paper proposes a production abnormal event diagnosis method based on digital twin technology to achieve intelligent tracing of the causes of production abnormal events and improve the problems of poor timeliness and lack of feedback mechanism for the diagnosis of production abnormal events of complex products. First, a complex product production abnormal event diagnosis model is constructed, in which physical production workshop, virtual production workshop, workshop twin data platform and abnormal event diagnosis service system work together. Second, the joint method of Bayesian network (BN) and C5.0 decision tree algorithm (C5.0) is used to achieve the diagnosis of production abnormal events. Finally, the feasibility and effectiveness of the scheme are verified by the arithmetic example of key stations in the bogie production workshop. The diagnostic accuracy of 92.49% of the BN–C5.0 model is higher than that of the conventional C5.0 decision tree model with 79.94% accuracy. The proposed method and mechanism provide a reference model for the application of digital twin to the diagnosis of abnormal production events.