基于人工智能,有限元分析和多体动力学模拟集成的行星变速箱的形状-性能集成监控技术的研究
Yanping Cui1, Boshuo An1, Zhe Wu1
1School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
一个新的数字双胞胎系统准确地预测了行星变速箱的性能,并在极端条件下识别了轮损坏. 这种虚拟模型增强了高速,高负载应用程序的操作状态识别和可靠性.
科学领域:
- 机械工程 机械工程
- 计算工程 计算工程 计算工程
- 人工智能的人工智能
背景情况:
- 在高速和高负载条件下,行星变速箱在性能监测方面面临着挑战.
- 轮牙损坏是一个重大问题,在操作过程中很难评估.
研究的目的:
- 开发一个数字双胞胎系统,用于行星变速箱的操作状态识别和性能预测.
- 将形态和功能特征集成为一个全面的虚拟模型.
主要方法:
- 使用有限元分析 (FEA),多体动力学 (MBD) 模拟和人工智能 (AI) 算法.
- 使用实验数据创建了变速箱几何,结构和动态的虚拟映射.
- 开发了一个结合结构性性能代用模型,系统驱动模型和动态性能数据库的系统.
主要成果:
- 在特定条件下,结构性能模型的最大误差为3%,对太阳轮网格评估的影响微不足道.
- 动态性能预测显示最大误差为1.68%,表明与实验数据有很强的一致性.
- 数字双胞胎系统成功地映射了变速箱的行为,并启用了状态识别.
结论:
- 拟议的数字双胞胎系统为行星变速箱提供了准确的性能预测和操作状态识别.
- 该系统有效地解决了在极端运行条件下监控的挑战.
- 这种方法提高了关键变速箱组件的可靠性和预测性维护能力.
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