Bearing Transfer Diagnosis Method Based on Digital Twin and Knowledge Graph
This paper presents a bearing transfer diagnosis method based on digital twin and knowledge graph.The target equa-tion is constructed by encapsulating the physical characteristics of the bearing and using digital twin technology,performing di-mensionality reduction based on an reduced-order model,and integrating multi-degree of freedom dynamics to simulate various fault conditions of the bearing.An end-to-end deep learning structure is employed to learn fault features in the digital twin data-set,and then fine-tuned using on-site experimental data through transfer learning,thereby overcoming the issue of limited data in industrial environments.By the knowledge graph,the diagnostic results are associated with fault causes and improvement strategies,the multi-angle analysis ability of the fault diagnosis outcomes is improved.This method realizes real-time state up-dates and performance evolution of the rolling bearing digital twin model,overcomes the drawbacks of insufficient bearing data and limited data processing capabilities in existing industrial environments.
digital twinbearing fault diagnosistransfer learningknowledge graph
万方数据
维普
