采用科尔莫哥罗夫-阿诺德网络的端面齿盘拉杆转子预紧参数识别

冯建欣; 余沛坰; 魏佳明; 文思果; 李浦; 袁奇

doi:10.7652/xjtuxb202505016

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采用科尔莫哥罗夫-阿诺德网络的端面齿盘拉杆转子预紧参数识别

—————————————————————— | 更新时间：2025-08-26

- 采用科尔莫哥罗夫-阿诺德网络的端面齿盘拉杆转子预紧参数识别
- Parameter Identification for Preloaded Tie-Bolt Rotors with Curvic Couplings Using Kolmogorov-Arnold Networks
- 西安交通大学学报 2025年59卷第5期页码：168-177
- 作者机构：
  
  1.西安交通大学能源与动力工程学院， 710049，西安
  2.杭州汽轮动力集团有限公司先进动力研究院， 310016，杭州
- 作者简介：
  
  冯建欣（2000—），男，硕士生；
  李浦（通信作者），男，副研究员。
- 基金信息：
  
  国家自然科学基金资助项目(12372058;12372057)
- DOI：10.7652/xjtuxb202505016
  中图分类号： TK14
- 收稿：2024-11-15，
  
  网络首发：2025-02-19，
  
  纸质出版：2025-05-10
- 稿件说明：
移动端阅览
冯建欣, 余沛坰, 魏佳明, 等. 采用科尔莫哥罗夫-阿诺德网络的端面齿盘拉杆转子预紧参数识别[J]. 西安交通大学学报, 2025,59(5):168-177.

FENG Jianxin, YU Peijiong, WEI Jiaming, et al. Parameter Identification for Preloaded Tie-Bolt Rotors with Curvic Couplings Using Kolmogorov-Arnold Networks[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 168-177.
冯建欣, 余沛坰, 魏佳明, 等. 采用科尔莫哥罗夫-阿诺德网络的端面齿盘拉杆转子预紧参数识别[J]. 西安交通大学学报, 2025,59(5):168-177. DOI： 10.7652/xjtuxb202505016.

FENG Jianxin, YU Peijiong, WEI Jiaming, et al. Parameter Identification for Preloaded Tie-Bolt Rotors with Curvic Couplings Using Kolmogorov-Arnold Networks[J]. Journal of Xi’an Jiaotong University, 2025, 59(5): 168-177. DOI： 10.7652/xjtuxb202505016.

摘要

为解决端面齿盘拉杆转子在预紧状态下的拉杆振动参数识别问题，提出了基于加速度计附加质量的拉杆频率解谐方法。开展了拉杆弯曲固有频率对加速度计附加质量位置的灵敏度分析，进行顺序、十字交叉和正反序2轮预紧3种不同方式下的残余预紧力识别和拉杆模态实验。构建了附加质量的拉杆梁单元模型并采用科尔莫哥罗夫-阿诺德神经网络（KAN）实现了不同预紧力下的拉杆频响函数预测和参数识别。研究结果表明：拉杆共振频率随预紧力增大而增大，同时传感器附加质量会降低共振频率，最大降幅达8%；正反序2轮预紧可显著降低拉杆预紧力分散度约10%，优于顺序加载和交叉加载；KAN网络能够有效预测不同预紧状态下拉杆的频响函数，谐振频率预测误差小于4.2%，同时识别刚度能够准确反映预紧失谐下的拉杆振动模态特性，为实际工程中拉杆预紧参数识别提供了一种实验方法和计算模型。

Abstract

To identify the vibration characteristics of tie-rods in preloaded tie-bolt rotors with curvic couplings

a tie-bolt frequency detuning method based on accelerometer-added mass was proposed. An analysis was carried out to check the sensitivity of the bending natural frequency of the tie-rod to the position of the accelerometer-added mass. Residual preload identification and modal impact tests were performed for three different preloading methods: forward-sequence

cross

and forward-reverse-sequence two-round preloading. A tie-rod beam element model with added mass was established

and the Kolmogorov-Arnold networks (KAN) were employed to predict the frequency response function and identify parameters under different preload conditions. The results indicate that the resonance frequency of the tie-rod increases with the increase of preload

while the added mass of the sensor reduces the resonance frequency up to 8%. Forward-reverse-sequence two-round preloading significantly reduces the dispersion of the tie-rod preload by approximately 10%

outperforming forward-sequence and cross preloading. The KAN are able to effectively predict the frequency response function of the tie-rod under different preload states

with a prediction error of less than 4.2% for resonant frequencies. Moreover

the identified stiffness accurately reflects the vibration modal characteristics of the tie-rod under preload detuning. Therefore

the experimental method and computational model are reliable for identifying preloading parameters in practical engineering applications.

关键词

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references

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