Dynamic Sensitivity Analysis and Response Prediction of Turbine Generator Shaft System

LI Dehao; CHEN Jiuru; JI Dawei; CHEN Gang; LI Pu; YUAN Qi

doi:10.7652/xjtuxb202604016

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Dynamic Sensitivity Analysis and Response Prediction of Turbine Generator Shaft System

Special Topic Dynamic Characteristics Research of Key Steam Turbine Components | 更新时间：2026-04-10

- Dynamic Sensitivity Analysis and Response Prediction of Turbine Generator Shaft System
- Journal of Xi'an Jiaotong University Vol. 60, Issue 4, Pages: 199-209(2026)
- 作者机构：
  
  西安交通大学能源与动力工程学院，710049，西安
  上海电气电站集团，310016，上海
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202604016
  CLC： TK14
- Received：03 September 2025，
  
  Online First：19 December 2025，
  
  Published：10 April 2026
- 稿件说明：
移动端阅览
LI Dehao, CHEN Jiuru, JI Dawei, et al. Dynamic Sensitivity Analysis and Response Prediction of Turbine Generator Shaft System[J]. Journal of Xi'an Jiaotong University, 2026, 60(4): 199-209.
DOI：

LI Dehao, CHEN Jiuru, JI Dawei, et al. Dynamic Sensitivity Analysis and Response Prediction of Turbine Generator Shaft System[J]. Journal of Xi'an Jiaotong University, 2026, 60(4): 199-209. DOI： 10.7652/xjtuxb202604016.

摘要

针对汽轮机组轴系动态响应特性的快速预测问题，通过有限元建模与混合卷积神经网络（HCNet）相结合的方法，开展了轴系动力学响应的灵敏度分析和预测。首先，基于虚拟材料法建立了考虑联轴器不对中刚度特性的汽轮机组轴系有限元模型；其次，考虑联轴器参数不确定性，针对联轴器转动惯量、质量、平行不对中量与角度不对中量4个参数，采用拉丁超立方采样方法，计算了不同参数组合下的轴系动力学响应并开展了Sobol灵敏度分析；最后，提出了一种新型HCNet模型，实现了轴系频响特性的高效预测。研究结果表明：振动响应峰值对角度不对中更加敏感，其在添加一阶、二阶不平衡量下的一阶灵敏度指数与总灵敏度指数分别为0.50、0.48与0.60、0.65，均显著高于其他参数。新型HCNet模型预测的轴系频响特性，其精度显著优于卷积神经网络、残差神经网络和Transformer这3种经典模型预测的轴系频响特性。该研究可为汽轮发电机组轴系动力学响应的快速预测与故障诊断提供理论支撑和分析方法。

Abstract

In order to address the issue of rapidly predicting the dynamic response characteristics of turbine generator shaft systems，sensitivity analysis and prediction of shaft system dynamic responses were conducted through a method combining finite element modeling and a hybrid convolutional neural network（HCNet）.First，a finite element model of the turbine generator shaft system was established based on the virtual material method，taking into account the misalignment stiffness characteristics of the coupling.Second，considering the uncertainty of coupling parameters，the Latin hypercube sampling method was applied for four parameters—coupling moment of inertia，mass，parallel misalignment，and angular misalignment—to compute the shaft system dynamic response under different parameter combinations，and Sobol sensitivity analysis was performed. Finally，a novel HCNet model was proposed，achieving efficient prediction of the shaft system frequency response characteristics.The results indicate that the peak vibration response is more sensitive to angular misalignment.Its first-order sensitivity index and total sensitivity index under the addition of first-order and second-order unbalanced masses are 0.50，0.48，and 0.60，0.65，respectively，both significantly higher than those of other parameters.The shaft system frequency response characteristics predicted by the new HCNet model are significantly more accurate than those predicted by three classical neural network models：convolutional neural networks，residual neural networks，and Transformers. This research provides theoretical support and analytical methods for the rapid prediction and fault diagnosis of the shaft system dynamic response in turbine generator sets.

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