数字孪生辅助的直升机尾传动系统轴承与传动轴故障诊断

李恒; 唐倩; 陈国旺; 王浩宇

doi:10.13224/j.cnki.jasp.20230818

数字孪生辅助的直升机尾传动系统轴承与传动轴故障诊断

doi: 10.13224/j.cnki.jasp.20230818

李恒^1,,
唐倩^1,*, ,,
陈国旺¹,
王浩宇^{1, 2}

1.
重庆大学机械与运载工程学院，重庆 400044
2.
国家能源集团神东煤炭集团高端设备研发中心，陕西榆林 719315

基金项目: 国家自然科学基金（51975073）

详细信息

作者简介:
李恒（1999-），男，硕士生，主要从事机械系统动力学仿真、故障诊断等研究。E-mail：17331091728@163.com

通讯作者:
唐倩（1969-），女，教授、博士生导师，博士，主要从事智能制造技术与装备等方面的研究。E-mail：tqcqu@cqu.edu.cn

中图分类号: V240.2
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-27
- 网络出版日期: 2024-06-03

Diagnosis of bearing and drive shaft faults in helicopter tail drive systems assisted by digital twin

LI Heng^1
,,
TANG Qian^{1,*
, ,},
CHEN Guowang¹,
WANG Haoyu^{1, 2}

1.
College of Mechanical and Vehicle Engineering，Chongqing University，Chongqing 400044，China
2.
High-end Equipment Research and Development Center，CHN Energy Shendong Coal Croup Company Limited，Yulin Shaanxi 719315，China

摘要

摘要:
针对直升机尾传动系统故障数据不平衡问题，提出一种基于数字孪生和迁移学习的直升机尾传动系统故障诊断方法。建立直升机尾传动系统刚柔耦合动力学仿真模型获取真实反映直升机尾传动系统工作状态的高保真故障仿真数据。通过引入坐标可分离卷积和注意力机制的残差网络进行故障特征提取和分类。采用基于高斯核函数的领域自适应方法缩小仿真数据和实验数据在特征空间的分布差异。为提高决策边界的鲁棒性，增强类别之间的区分度，引入边界正则化的交叉熵损失。经实验验证，基于数字孪生和迁移学习的故障诊断方法，可以解决数据不平衡导致的深度学习故障诊断模型训练效果变差的问题，使模型损失显著降低、准确率至少提高了2.17%，达到基于正常数据驱动的深度学习故障诊断模型的性能水平。
- 不平衡数据 /
- 故障诊断 /
- 数字孪生 /
- 迁移学习 /
- 直升机尾传动系统
Abstract:
Considering the problem of unbalanced fault data in the helicopter tail drive system, a fault diagnosis method of helicopter tail drive system based on digital twin and transfer learning was proposed. A rigid-flexible coupling dynamics simulation model of the helicopter tail drive system was established to obtain high-fidelity fault simulation data truly reflecting the operating state of the helicopter tail drive system. A residual network introducing coordinate separable convolution and attention mechanism was used for fault feature extraction and classification. The domain adaptive method based on Gaussian kernel function was used to reduce the distribution difference between simulation data and experimental data in the feature space. In order to improve the robustness of the decision boundary and enhance the differentiation between categories, the cross-entry loss with margin regularization was introduced. It was experimentally verified that the fault diagnosis method based on digital twin and transfer learning can address the issue of deteriorated training effect in deep learning fault diagnosis model caused by unbalanced data. This method significantly reduced model loss and improved model accuracy by at least 2.17%, reaching the performance level of the deep learning fault diagnosis model based on normal data.
- unbalanced data /
- fault diagnosis /
- digital twin /
- transfer learning /
- helicopter tail drive system

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图 1 直升机尾传动系统刚柔耦合动力学仿真模型

Figure 1. Rigid-flexible coupled dynamics simulation model of helicopter tail drive system

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图 2 刚柔耦合动力学仿真模型建模

Figure 2. Modeling process of rigid-flexible coupled dynamics simulation model

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图 3 CSARNet模型结构图

Figure 3. Structure of the CSARNet model

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图 4 基于数字孪生和迁移学习的故障诊断方法整体框架

Figure 4. Fault diagnosis framework based on digital twin and transfer learning

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图 5 直升机尾传动系统模拟实验台

Figure 5. Helicopter tail drive system simulation test bench

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图 6 实验台测试点位

Figure 6. Lab bench test point location

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图 7 各类型故障实验件

Figure 7. Test pieces for each type of fault

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图 8 各类型故障仿真件

Figure 8. Simulation pieces for each type of fault

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图 9 仿真结果与实验结果在时域和频域下的对比

Figure 9. Comparison of simulation results with experimental results in time and frequency domains

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图 10 验证集准确率和损失变化曲线

Figure 10. Variation curves of accuracy and loss on the validation set

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图 11 各模型验证集准确率对比曲线

Figure 11. Comparison curves of accuracy of validation sets for each model

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图 12 3种工况下各组实验训练过程中验证集上的准确率和损失

Figure 12. Accuracy and loss on the validation set during the training for each set of experiments under the three working conditions

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图 13 3组实验在测试集上的T-SNE分类可视化

Figure 13. Visualization of T-SNE classification on the data test set for three sets of experiments

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图 14 3组实验在测试集上的混淆矩阵

Figure 14. Confusion matrix on the data test set for the three sets of experiments

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表 1 刚性体动力学仿真模型约束关系

Table 1. Constraints of the rigid body dynamics simulation model

对象1	对象2	约束
输入机匣	Ground（地面）	固定副
角接触球轴承外圈	输入机匣	固定副
角接触球轴承内圈	输入轴	固定副
花键	空心轴	固定副
叠片联轴器	空心轴	固定副
叠片联轴器	花键	固定副
叠片联轴器	深沟球轴承内圈	固定副
深沟球轴承外圈	轴承座	固定副
套筒	中减机匣	固定副
角接触球轴承外圈	套筒	固定副
角接触球轴承内圈	轴	固定副
弧齿锥齿轮	轴	固定副
中减机匣	Ground（地面）	固定副
输入轴	Ground（地面）	旋转副+驱动
轴承内圈/外圈	滚子	接触力
弧齿锥齿轮	弧齿锥齿轮	接触力
输出轴	Ground（地面）	旋转副+负载

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表 2 不同工况下仿真结果和实验结果对比

Table 2. Comparison between simulation results and experimental results under different operating conditions

测试方位	输入转速/（r/min）	加速度/（m/s²）		误差/%
测试方位	输入转速/（r/min）	测试结果有效值	仿真结果有效值	误差/%
中减减速器水平	2000	25.04	25.58	2.1
中减减速器垂直		20.79	22.68	9.1
轴承座水平		15.67	18.52	18.2
轴承座垂直		13.00	9.89	23.9
中减减速器水平	2500	34.83	31.88	8.5
中减减速器垂直		29.64	28.84	2.7
轴承座水平		18.80	22.39	19.1
轴承座垂直		17.72	14.40	18.8
中减减速器水平	3000	35.02	41.20	17.6
中减减速器垂直		35.82	37.96	6.0
轴承座水平		19.98	23.67	18.5
轴承座垂直		19.87	15.22	23.4
中减减速器水平	3500	43.86	46.20	5.3
中减减速器垂直		44.63	46.92	5.1
轴承座水平		23.13	27.30	18.0
轴承座垂直		22.13	18.28	17.4
中减减速器水平	4000	48.85	57.32	17.3
中减减速器垂直		58.03	54.01	6.9
轴承座水平		34.49	39.28	13.9
轴承座垂直		36.51	30.16	17.4

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表 3 仿真数据各故障类型样本量设置

Table 3. Sample size for each fault type in the simulation data

故障类型	样本量
故障类型	轴承内圈裂纹	轴承外圈裂纹	轴承内圈磨损	轴承外圈磨损	轴穿孔	正常
训练集	600	600	600	600	600	600
验证集	200	200	200	200	200	200
测试集	200	200	200	200	200	200

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表 4 各模型仿真数据测试集准确率

Table 4. Accuracy of each model on the simulation data test set

模型	准确率/%			平均值/%
模型	S₁	S₂	S₃	平均值/%
CSARNet	97.66	99.75	99.42	98.94
ResNet	87.67	99.25	93.58	93.5
InceptionTime	97.58	99.58	98.58	98.58
FCN	99.83	98.83	96.67	98.44
GRU_FCN	96.42	99.58	96.5	97.5
LSTM_FCN	99.16	99.83	97.08	98.69
TSSequencer	65.417	74	79.25	72.89
1DCNN	71.92	74.58	72.67	73.06

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表 5 实验数据各故障类型样本量设置

Table 5. Sample size for each fault type in experimental data

数据类型	样本量
数据类型	轴承内圈裂纹	轴承外圈裂纹	轴承内圈磨损	轴承外圈磨损	轴穿孔	正常
不平衡训练集	60	60	60	60	60	600
正常训练集	600	600	600	600	600	600
验证集	200	200	200	200	200	200
测试集	200	200	200	200	200	200

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