基于数字孪生的汽车自动化生产线故障诊断研究OA

Traditional machine learning algorithms used for fault diagnosis in the automated production lines of automobiles require that the training and test sets have the same distribution and need a substantial number of training samples.However,in practice,fault sample data are difficult to acquire,and the oper-ating conditions of production lines are highly variable,leading to a low fault classification accuracy.In view of these problems,this paper proposes a research method for fault diagnosis in automated automobile production lines based on Digital Twin(DT)technology.This method initially models the actual produc-tion lines using SolidWorks,followed by rendering through Unity 3D software,and combines with PLC for DT model simulation.Finally,the method utilizes transfer learning techniques and convolutional neural networks to achieve fault diagnosis.The feasibility of the proposed method is verified by comparison with existing methods.