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电机数字孪生模型构建与温度预测

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电机的健康状态时刻关乎生产设备的运转效率与产品质量,电机出现严重故障时可能导致生产停滞与人员伤亡.传统的电机监测技术一般需要人工定期巡检,监测周期较长,不能及时发现电机热故障导致电机损坏.数字孪生技术给电机健康预测提供了新的解决思路,通过建立物理样机的数字孪生体,并对孪生模型数据进行分析,可模拟实际电机在不同工况下电磁损耗与温度变化,从而实现电机温度预测.
Construction of Digital Twin Model for Electric Motors and Temperature Prediction
The health status of the motor is always related to the efficiency of production equipment operation and the quality of the product.If the motor malfunctions seriously,it may lead to production stagnation and casualties.Traditional motor monitoring technology generally requires regular manual inspections,with a long monitoring cycle that cannot detect thermal faults in the motor in a timely manner,resulting in motor damage.Digital twin technology provides a new solution for motor health prediction.By establishing digital twins of physical prototypes,analyzing twin model data,simulating electromagnetic losses and temperature changes of actual motors under different working conditions,motor temperature prediction can be achieved.

digital twinpermanent magnet synchronous motortemperature predictionmodel construction

张迅、王建生、康献民、罗源昌、亢宗楠、李宏宇

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五邑大学 智能制造学部,广东 江门 529000

数字孪生 永磁同步电机 温度预测 模型构建

2021年度广东省普通高校重点领域专项(新一代信息技术)

2021ZDZX1045

2024

机械工程与自动化
山西省机电设计研究院 山西省机械工程学会

机械工程与自动化

影响因子:0.251
ISSN:1672-6413
年,卷(期):2024.(1)
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