一个GRU-KAN替代模型与基因算法统一采样为主动磁轴承-旋转器临界速度预测
Jiahang Cui1, Jianghong Li1,2, Feichao Cai1,3
1School of Power and Energy, Northwestern Polytechnical University, Xi'an 710129, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
一种新的混合型GRU-KAN模型快速准确地预测了主动磁轴承 (AMB) 转子动态,克服了传统有限元模型 (FEM) 对于高速应用的局限性.
科学领域:
- 机械工程 机械工程
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 活性磁轴承 (AMB) 对于高速旋转的机器至关重要.
- 了解转子动力学对于AMB性能和稳定性至关重要.
- 传统的有限元素建模 (FEM) 在分析控制器参数对动态的影响方面是计算密集的.
研究的目的:
- 开发一个快速准确的替代模型,用于AMB系统中的高速旋转器动态.
- 为了研究合控制增益对转子动态行为的影响.
- 为传统的FEM提供一个有效的替代品,用于临界速度估计.
主要方法:
- 开发了一个混合封闭的循环单元-科尔摩戈罗夫-阿诺德网络 (GRU-KAN) 替代模型.
- 一个基因算法驱动的统一设计采样策略被用于模型概括.
- 将GRU-KAN模型与支持向量回归和Kriging替代品进行了比较.
主要成果:
- 格鲁-坎替代模型实现了高的确定系数 (R2=0.9887) 和低的残留值.
- 实验验证显示,临界速度预测的平均绝对误差为38.51rpm.
- 代孕模型评估时间显著减少,为1.14×10-4秒,而FEM的时间为201秒.
结论:
- 拟议的GRU-KAN替代模型提供了高效率,准确性和全面的预测能力.
- 这种方法可以在AMB转子系统中快速估计关键速度.
- 这些发现有助于优化高速AMB系统的设计和控制.
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