基于数字孪生的矿井提升机天轮结构性能监测

张文豪; 吴娟; 阮锴燚

doi:10.13272/j.issn.1671-251x.2024050086

基于数字孪生的矿井提升机天轮结构性能监测

doi: 10.13272/j.issn.1671-251x.2024050086

张文豪^{1, 2, 3,},
吴娟^{1, 2, 3},
阮锴燚⁴

1.
太原理工大学机械与运载工程学院，山西太原　030024
2.
太原理工大学山西省矿山流体控制工程实验室，山西太原　030024
3.
太原理工大学矿山流体控制国家地方联合工程实验室，山西太原　030024
4.
北京理工大学机械与车辆学院，北京，100081

基金项目: 国家自然科学基金面上项目（52274156）。

详细信息

作者简介:
张文豪（1998—），男，河南周口人，硕士研究生，主要研究方向为煤矿智能化建设，E-mail：zwhao185@163.com

中图分类号: TD633
计量
- 文章访问数: 122
- HTML全文浏览量: 36
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-30
- 修回日期: 2024-08-11
- 网络出版日期: 2024-08-02

Structural performance monitoring of mine hoist head sheave based on digital twins

ZHANG Wenhao^{1, 2, 3
,},
WU Juan^{1, 2, 3},
RUAN Kaiyi⁴

1.
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2.
Shanxi Mine Fluid Control Engineering Laboratory, Taiyuan University of Technology, Taiyuan 030024, China
3.
National-local Joint Engineering Laboratory of Mining Fluid Control, Taiyuan University of Technology, Taiyuan 030024, China
4.
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

摘要

摘要: 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。
- 矿井提升机天轮 /
- 数字孪生 /
- 天轮结构性能监测 /
- 组合代理模型 /
- 天轮应变
Abstract: 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.
- mine hoist head sheave /
- digital twin /
- structural performance monitoring of head sheave /
- combined surrogate model /
- head sheave strain

HTML全文

图 1 天轮数字孪生监测系统结构

Figure 1. Structure of digital twin monitoring system for head sheave

下载: 全尺寸图片幻灯片

图 2 天轮结构性能预测模型构建流程

Figure 2. Construction process of structural performance prediction model of head sheave

下载: 全尺寸图片幻灯片

图 3 虚拟空间构建流程

Figure 3. Virtual space construction process

下载: 全尺寸图片幻灯片

图 4 数据传输过程

Figure 4. Date transmission process

下载: 全尺寸图片幻灯片

图 5 天轮运行状态监测

Figure 5. Operation status monitoring of head sheave

下载: 全尺寸图片幻灯片

图 6 天轮结构性能监测

Figure 6. Structural performance monitoring of head sheave

下载: 全尺寸图片幻灯片

图 7 故障预警

Figure 7. Fault warning

下载: 全尺寸图片幻灯片

图 8 天轮应变测量

Figure 8. Strain measurement of head sheave

下载: 全尺寸图片幻灯片

图 9 各测试点应变曲线

Figure 9. Strain curves of each testing point

下载: 全尺寸图片幻灯片

表 1 应变误差评估

Table 1. Strain error evaluation

应变对比	R²				均值
应变对比	测试点1	测试点2	测试点3	测试点4	均值
测量与预测	0.937 89	0.980 05	0.992 06	0.985 93	0.973 98
测量与仿真	0.940 84	0.980 13	0.992 26	0.992 20	0.976 36
仿真与预测	0.999 68	0.999 76	0.999 88	0.999 87	0.999 80

下载: 导出CSV

参考文献(21)

[1]	王琛,杨岸. 矿井提升机健康状态评估与预测系统研究[J]. 工矿自动化,2023,49(10):75-86. WANG Chen,YANG An. Research on the health evaluation and prediction system for mine hoists[J]. Journal of Mine Automation,2023,49(10):75-86.
[2]	滕虎. 提升机天轮状态监测与故障诊断研究[D]. 徐州:中国矿业大学,2023. TENG Hu. Research on condition monitoring and fault diagnosis of hoist crown wheel[D]. Xuzhou:China University of Mining and Technology,2023.
[3]	蔡晓炜,徐靖雯,杨朋霖,等. 提升机天轮状态监测与故障诊断系统[J]. 煤矿机械,2020,41(1):160-162. CAI Xiaowei,XU Jingwen,YANG Penglin,et al. State monitoring and fault diagnosis system of hoist head sheave[J]. Coal Mine Machinery,2020,41(1):160-162.
[4]	李瑶,涂兴子,吴娟,等. 基于线结构光的天轮偏摆监测机器视觉系统设计[J]. 煤炭工程,2020,52(5):178-182. LI Yao,TU Xingzi,WU Juan,et al. Design of machine vision system for head sheave deflection monitoring based on linear structured light[J]. Coal Engineering,2020,52(5):178-182.
[5]	姜雪,王超,王绍芝,等. 煤矿主井提升机滚筒与天轮故障监测系统[J]. 煤矿机械,2020,41(9):174-176. JIANG Xue,WANG Chao,WANG Shaozhi,et al. Fault monitoring system for drum and sheave wheel of main shaft hoist in coal mine[J]. Coal Mine Machinery,2020,41(9):174-176.
[6]	CONCETTA S,MARIO L,HERVE P,et al. Digital twin paradigm:a systematic literature review[J]. Computers in Industry,2021,130. DOI: 10.1016/J.COMPIND.2021.103469.
[7]	LAI Xiaonan,WANG Shuo,GUO Zhenggang,et al. Designing a shape-performance integrated digital twin based on multiple models and dynamic data:a boom crane example[J]. Journal of Mechanical Design,2021,143(7). DOI: 10.1115/1.4049861.
[8]	HE Wenbin,MAO Jianxu,SONG Kai,et al. Structural performance prediction based on the digital twin model:a battery bracket example[J]. Reliability Engineering and System Safety,2023,229. DOI:10.1016/J.RESS.2022. 108874. 108874.
[9]	赵佰亭,施建国,贾晓芬. 井筒提升机数字孪生系统研究[J/OL]. 系统仿真学报:1-13[2024-08-08]. https://doi. org/10.16182/j. issn1004731x. joss. 23-0556. ZHAO Baiting,SHI Jianguo,JIA Xiaofen. Research on digital twin system of rockshaft hoist[J/OL]. Journal of System Simulation:1-13[2024-08-08]. https://doi.org/10.16182/j.issn1004731x.joss.23-0556.
[10]	刘明浩,岳彩旭,夏伟,等. 基于数字孪生的铣刀状态实时监控[J]. 计算机集成制造系统,2023,29(6):2118-2129. LIU Minghao,YUE Caixu,XIA Wei,et al. Real-time monitoring of milling tool state based on digital twin[J]. Computer Integrated Manufacturing Systems,2023,29(6):2118-2129.
[11]	LIANG Xuejun,WU Juan,RUAN Kaiyi. Simulation modeling and temperature over-advance perception of mine hoist system based on digital twin technology[J]. Machines,2023,11(10). DOI: 10.3390/MACHINES11100966.
[12]	丁恩杰,俞啸,夏冰,等. 矿山信息化发展及以数字孪生为核心的智慧矿山关键技术[J]. 煤炭学报,2022,47(1):564-578. DING Enjie,YU Xiao,XIA Bing,et al. Development of mine informatization and key technologies of intelligent mines[J]. Journal of China Coal Society,2022,47(1):564-578.
[13]	SEMENOV Y,SEMENOVA O,KUVATAEV I. Solutions for digitalization of the coal industry implemented in UC kuzbassrazrezugol[J]. E3S Web of Conferences,2020,174. DOI: 10.1051/e3sconf/202017401042.
[14]	陶飞,刘蔚然,张萌,等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统,2019,25(1):1-18. TAO Fei,LIU Weiran,ZHANG Meng,et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems,2019,25(1):1-18.
[15]	WANG Zili,GU Yuchen,ZHANG Shuyou,et al. A transferred hybrid surrogate model integrating Gaussian membership virtual sample generation for small sample prediction:applications in metal tube bending[J]. Engineering Applications of Artificial Intelligence,2024,129. DOI: 10.1016/J.ENGAPPAI.2023.107560.
[16]	李春明,孙晓霞,张涛,等. 基于组合代理模型的变海拔工况车辆动力总成流动性能优化[J]. 机械工程学报,2023,59(4):135-144. doi: 10.3901/JME.2023.04.135 LI Chunming,SUN Xiaoxia,ZHANG Tao,et al. Optimization of powertrain mobility performance of vehicles with variable altitude working conditions by ensemble of surrogate models[J]. Journal of Mechanical Engineering,2023,59(4):135-144. doi: 10.3901/JME.2023.04.135
[17]	BELKHABBAZ A,GUEGUIN M,HAFID F,et al. Surrogate model based approach to predict fatigue stress field in multi-stranded cables[J]. International Journal of Solids and Structures,2021,230/231. DOI: 10.1016/J.IJSOLSTR.2021.111168.
[18]	游雄雄,牛占文. 基于组合模型的复杂系统超多目标优化算法[J]. 计算机集成制造系统,2024,30(4):1201-1212. YOU Xiongxiong,NIU Zhanwen. Ensemble surrogateassisted evolutionary algorithm for complex system many-objective optimization[J]. Computer Integrated Manufacturing Systems,2024,30(4):1201-1212.
[19]	何西旺,杨亮亮,冉仁杰,等. 基于多评价标准的代理模型综合比较研究[J]. 机械工程学报,2022,58(16):403-419. doi: 10.3901/JME.2022.16.403 HE Xiwang,YANG Liangliang,RAN Renjie,et al. Comparative studies of surrogate models based on multiple evaluation criteria[J]. Journal of Mechanical Engineering,2022,58(16):403-419. doi: 10.3901/JME.2022.16.403
[20]	吕利叶,鲁玉军,王硕,等. 代理模型技术及其应用:现状与展望[J]. 机械工程学报,2024,60(3):254-281. LYU Liye,LU Yujun,WANG Shuo,et al. Survey and prospect of surrogate model technique and application[J]. Journal of Mechanical Engineering,2024,60(3):254-281.
[21]	吕利叶. 先进代理模型方法与应用研究[D]. 大连:大连理工大学,2020. LYU Liye. On advanced surrogate models and application[J]. Dalian:Dalian University of Technology,2022.