College of Mechanical and Vehicle Engineering, Taiyuan University of Technology
Shanxi Mine Fluid Control Engineering Laboratory, Taiyuan University of Technology
National-local Joint Engineering Laboratory of Mining Fluid Control, Taiyuan University of Technology
School of Mechanical Engineering, Beijing Institute of Technology

目前矿井提升机天轮的监测研究大多侧重于对天轮的振动、温度、偏摆的监测，而对天轮结构性能监测的研究较少。针对该问题，提出了一种基于数字孪生的矿井提升机天轮结构性能监测方法。根据矿井提升机天轮运行过程中的实际状况，设计了矿井提升机天轮数字孪生监测系统，该系统由物体实体层、孪生模型层、孪生数据层、应用层及各层之间的连接组成，其中孪生数据层中的预测数据是矿井提升机天轮运行过程中通过天轮结构性能预测模型实时预测的天轮结构性能数据，包括应力和应变数据。矿井提升机天轮结构性能预测模型采用组合代理模型构建：采用处理后的有限元数据训练得到径向基函数（RBF）单一代理模型，基于广义均方误差求得单一代理模型在组合代理模型中的权重，从而得到天轮结构性能预测模型。以立井五绳摩擦提升系统为试验对象，基于Unity3D平台，通过虚拟空间、数据传输及应用模块的构建，建立了矿井提升机天轮数字孪生监测系统，试验结果表明：在天轮运行过程中，4个测试点的测量应变和预测应变平均决定系数为0.973 98，预测应变与测量应变具有较高的相关性，验证了设计的预测模型能够满足对天轮结构性能监测的需求。

Currently, most of the monitoring research on the head sheave of mine hoists focuses on monitoring the vibration, temperature, and deflection of the head sheave, while there is relatively little research on monitoring the structural performance of the head sheave. In order to solve the above problems, a structural performance monitoring method of mine hoist head sheave based on digital twins is proposed. Based on the actual conditions during the operation of the mine hoist head sheave, a digital twin monitoring system for the mine hoist head sheave is designed. The system consists of an object entity layer, a twin model layer, a twin data layer, an application layer, and connections between each layer. The predicted data in the twin data layer is the real-time head sheave structural performance data predicted by the head sheave structural performance prediction model during the operation of the mine hoist head sheave, including stress and strain data. The structural performance prediction model of the mine hoist head sheave is constructed using a combined surrogate model. A single surrogate model of radial basis function (RBF) is trained using processed finite element data. The weight of the single surrogate model in the combined surrogate model is obtained based on generalized mean square error, thus obtaining the head sheave structural performance prediction model. Taking the five rope friction lifting system of the vertical shaft as the experimental object, based on the Unity3D platform, a digital twin monitoring system for the mine hoist head sheave is established through the construction of virtual space, data transmission, and application modules. The experimental results show that during the operation of the head sheave, the average determination coefficient of the measured strain and predicted strain at the four test points is 0.973 98, indicating a high correlation between the predicted strain and the measured strain. This verifies that the designed prediction model can meet the needs of monitoring the structural performance of the head sheave.

国家自然科学基金面上项目（52274156）。

张文豪，吴娟，阮锴燚. 基于数字孪生的矿井提升机天轮结构性能监测[J]. 工矿自动化，2024，50（8）：69-75.

ZHANG Wenhao, WU Juan, RUAN Kaiyi. Structural performance monitoring of mine hoist head sheave based on digital twins[J]. Journal of Mine Automation，2024，50（8）：69-75.