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1.河南科技大学 机电工程学院, 河南 洛阳 471003
2.机械装备先进制造河南省协同创新中心, 河南 洛阳 471003
3.郑州众邦超硬工具有限公司, 河南 郑州 450001
王松林(1995— ),男,河南永城人,硕士;主要研究方向为齿轮智能制造、数字孪生技术;1137172685@qq.com。
李聚波(1979— ),男,河南南阳人,博士,副教授;主要研究方向为齿轮智能制造、数字孪生等;lipo168201@163.com。
纸质出版日期:2023-08-15,
收稿日期:2022-08-11,
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王松林,徐爱军,李聚波等.基于数字孪生模型的齿轮试验台状态监测方法[J].机械传动,2023,47(08):142-147.
Wang Songlin,Xu Aijun,Li Jubo,et al.Condition Monitoring Method of Gear Test-bed Based on a Digital Twin Model[J].Journal of Mechanical Transmission,2023,47(08):142-147.
王松林,徐爱军,李聚波等.基于数字孪生模型的齿轮试验台状态监测方法[J].机械传动,2023,47(08):142-147. DOI: 10.16578/j.issn.1004.2539.2023.08.020.
Wang Songlin,Xu Aijun,Li Jubo,et al.Condition Monitoring Method of Gear Test-bed Based on a Digital Twin Model[J].Journal of Mechanical Transmission,2023,47(08):142-147. DOI: 10.16578/j.issn.1004.2539.2023.08.020.
为了对齿轮试验台运行状态进行监测,及时有效地发现运行故障,保证设备稳定地运行,提出了一种基于数字孪生的齿轮试验台状态监测方法。通过建立齿轮试验台的数字孪生模型,实时仿真齿轮试验台在正常运行工况下的运行状态,实现对齿轮试验台设备状态监测、故障判断和运行状态的预测。经闭功率流齿轮试验台测试结果对比表明,齿轮试验台数字孪生体仿真结果与齿轮试验台测试结果基本一致,此技术可以还原正常运行工况下齿轮试验台的运行状况,从而实现对齿轮试验台的状态监测,为故障诊断、状态监测提供了一种新方法。
In order to monitor the running state of the gear test-bed
find the running faults in time and effectively
and ensure the stable operation of the equipment
a state monitoring method of the gear test-bed based on digital twin is proposed. By establishing the digital twin model of the gear test-bed
the running state of the gear test-bed under normal operating conditions is simulated in real time
so as to realize the state monitoring
fault judgment and operation state prediction of the gear test-bed equipment. The comparison of the test results of the closed power flow gear test-bed shows that the digital twin simulation results of the gear test-bed are basically consistent with the test results of the gear test-bed. This technology can restore the operating conditions of the gear test-bed under normal operating conditions
so as to realize the state monitoring of the gear test-bed
and provide a new method for fault diagnosis and state monitoring.
数字孪生齿轮试验台状态监测
Digital twinGear test-bedCondition monitoring
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