基于数字孪生的机电液系统PHM关键技术综述

Digital Twin for Complex Electromechanical-hydraulic System PHM:A Review

数字孪生(Digital twin,DT)技术与预测和健康管理(Prognostics and health management,PHM)技术是智能制造领域中的两个热点研究方向。在对PHM技术现状总结分析的基础上,归纳当前制约PHM技术发展和应用的关键性问题如下:设备故障机理研究不透彻、全生命周期数据不完备、健康状态监测方法不足、多层级状态信息综合不足以及不确定性管理问题。并阐述数字孪生技术在解决这些问题过程中的独特优势,提出将基于第一性原理的多维数字孪生模型构建、虚实空间的多维数据映射、孪生体技术状态一致性度量与模型的高效迭代修正以及基于多域特征的系统健康评估、预测与维护决策作为关键技术构建DT-PHM研究架构。随着技术不断推进与发展,两项技术深度融合,基于数字孪生的复杂系统健康管理技术必将成为未来装备全生命周期视情维修和预测性维修的关键技术之一。

Digital twin technology and PHM technology are two hot research directions in the field of intelligent manufacturing.On the basis of summarizing and analyzing the current status of PHM technology,this study summarizes the key issues that currently restrict the development and application of PHM technology,including incomplete research on equipment failure mechanisms,incomplete data throughout the entire lifecycle,insufficient methods for monitoring health status,insufficient synthesis of multi-level status information,and uncertainty management issues.And it elaborates on the unique advantages of digital twin technology in solving these shortcomings,and proposes the construction of multi-dimensional digital twin models based on first principles,multi-dimensional data mapping in virtual and real spaces,efficient iterative correction of twin technology state consistency measurement and model,and system health assessment,prediction,and maintenance decision-making based on multi domain features as key technologies to construct the DT-PHM research architecture.With the continuous advancement and development of technology,the two technologies are constantly deeply integrated.The complex system health management technology based on digital twins will undoubtedly become one of the key technologies for future equipment full lifecycle maintenance and predictive maintenance.