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基于改进的SENet航空发动机振动预测

夏存江 詹于游

夏存江, 詹于游. 基于改进的SENet航空发动机振动预测[J]. 航空动力学报, 2022, 37(12):2807-2817 doi: 10.13224/j.cnki.jasp.20220110
引用本文: 夏存江, 詹于游. 基于改进的SENet航空发动机振动预测[J]. 航空动力学报, 2022, 37(12):2807-2817 doi: 10.13224/j.cnki.jasp.20220110
XIA Cunjiang, ZHAN Yuyou. Vibration prediction of aeroengines based on enhanced SENet model[J]. Journal of Aerospace Power, 2022, 37(12):2807-2817 doi: 10.13224/j.cnki.jasp.20220110
Citation: XIA Cunjiang, ZHAN Yuyou. Vibration prediction of aeroengines based on enhanced SENet model[J]. Journal of Aerospace Power, 2022, 37(12):2807-2817 doi: 10.13224/j.cnki.jasp.20220110

基于改进的SENet航空发动机振动预测

doi: 10.13224/j.cnki.jasp.20220110
基金项目: 四川省科技基金(2022YFG0356); 西藏科技厅重点研发计(XZ202101ZY0017G); 民航局教育培训项目(0252001);中央高校基本科研业务费基金项目(J2022-014)
详细信息
    作者简介:

    夏存江(1971-),男,教授、硕士生导师,硕士,研究方向为航空发动机控制与维修技术。E-mail:xia-cunjiang@aemtc.com

    通讯作者:

    詹于游(1997-),男,硕士生,研究方向为航空宇航推进理论与工程。E-mail:zyyskma@163.com

  • 中图分类号: V239

Vibration prediction of aeroengines based on enhanced SENet model

  • 摘要:

    为实时监测和预警航空发动机振动状态,基于气路及振动参数,提出一种使用改进的SENet(squeeze-and-excitation network)模型,对航空发动机近未来的振动进行预测。该研究相比以往采用的实验室模拟数据和仿真数据,使用了真实的QAR(quick access recorder)数据并进行随机采样,以求更能表征发动机振动和工作参数之间的关系。同时,不仅使用其他振动信号进行验证,还在其他型号的发动机上进行测试。结果表明:针对航空发动机的振动进行预测是可行的,SENet模型可以有效并实时追踪振动的突变和波动。此外,该方法对于其他振动信号和不同类型的发动机具有一定的适用性。而且相较于以往采用的其他经典的深度模型,SENet模型在振动的预测中能得到更小的误差。实验证明,相较于以往只使用振动这个单参数进行预测,并行使用与振动相关的多参数融合进行研究更能提高预测的准确性。

     

  • 图 1  各插值法对比

    Figure 1.  Comparison results of interpolation methods

    图 2  缺失值补全前后对比图

    Figure 2.  Comparison results of complete data and missing data

    图 3  残差学习:构建残差块

    Figure 3.  Residual learning: a building block

    图 4  注意力机制

    Figure 4.  Attention mechanism

    图 5  SE-ResNet 模块

    Figure 5.  SE-ResNet module

    图 6  网络架构示例

    Figure 6.  Example network architectures

    图 7  余弦退火学习率

    Figure 7.  Cosine annealing learning rate

    图 8  SENet训练效果

    Figure 8.  Training effect of SENet

    图 9  总体预测结果

    Figure 9.  Overall prediction results

    图 10  局部预测效果 (1)

    Figure 10.  Partial prediction results (1)

    图 11  局部预测结果 (2)

    Figure 11.  Partial prediction results (2)

    图 12  1号轴承支撑振动传感器N1的预测误差分布(同一类型发动机)

    Figure 12.  Prediction error distribution of bearing No.1 support vibration sensor N1 (same type of aeroengine )

    图 13  其他振动参数预测结果

    Figure 13.  Prediction of the other vibration parameters

    图 14  1号轴承支撑振动传感器高压转子预测误差分布(同一类型发动机)

    Figure 14.  Prediction error distribution of bearing No.1 support vibration sensor for high pressure rotor (same type of aeroengine )

    图 15  不同类型发动机预测结果

    Figure 15.  Prediction results of different types of aeroengine

    图 16  1号轴承支撑振动传感器N1的预测误差分布(不同类型发动机)

    Figure 16.  Prediction error distribution of bearing No.1 support vibration sensor N1 (different types of aeroengine)

    图 17  未来15步的预测结果

    Figure 17.  Prediction results for the next 15 steps

    图 18  未来20步的预测结果

    Figure 18.  Prediction results for the next 20 steps

    表  1  神经网络模型参数

    Table  1.   Parameters of neural network model

    层号参数其他
    17×7,64步幅:1,
    填充:3
    3×3,Max pool步幅:2,
    填充:1
    2$ \left( \begin{gathered} 3 \times 3,64 \\ 3 \times 3,64 \\ \end{gathered} \right) \times 2 $所有卷积层:
    步幅:1,
    填充:1
    3$ \left( \begin{gathered} 3 \times 3,128 \\ 3 \times 3,128 \\ \end{gathered} \right) \times 2 $第1个卷积层:
    步幅:(1, 2),
    填充:1
    4$ \left( \begin{gathered} 3 \times 3,256 \\ 3 \times 3,256 \\ \end{gathered} \right) \times 2 $第1个卷积层:
    步幅:(1, 2),
    填充:1
    5$ \left( \begin{gathered} 3 \times 3,512 \\ 3 \times 3,512 \\ \end{gathered} \right) \times 2 $第1个卷积层:
    步幅:(1, 2),
    填充:1
    6Global average pooling(1, 1)
    7FC512×10
    下载: 导出CSV

    表  2  多模型损失对比

    Table  2.   Comparison of multi model loss

    模型测试集损失
    SENet0.0203
    单参数SENet2.5070
    ResNet0.0210
    VggNet0.0261
    MLP0.0243
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-04
  • 网络出版日期:  2022-11-09

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