Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest

Abstract

Digital twin (DT) is a key technology for realizing the interconnection and intelligent operation of the physical world and the world of information and provides a new paradigm for fault diagnosis. Traditional machine learning algorithms require a balanced dataset. Training and testing sets must have the same distribution. Training a good generalization model is difficult in an actual production line operation process. Fault diagnosis technology based on the digital twin uses its ultrarealistic, multisystem, and high-precision characteristics to simulate fault data that are difficult to obtain in an actual production line to train a reliable fault diagnosis model. In this article, we first propose an improved random forest (IRF) algorithm, which reselects decision trees with high accuracy and large differences through hierarchical clustering and gives them weights. Digital twin technology is used to simulate a large number of balanced datasets to train the model, and the trained model can be transferred to a physical production line through transfer learning for fault diagnosis. Finally, the feasibility of our proposed algorithm is verified through a case study of an automobile rear axle assembly line, for which the accuracy of the proposed algorithm reaches 97.8%. The traditional machine learning plus digital twin fault diagnosis method proposed in this paper involves some generalization, and thus has practical value when extended to other fields.

Keywords

• fault diagnosis
• digital twin
• random forest
• hierarchical clustering
• transfer learning