• Chinese Journal of Ship Research
  • Vol. 19, Issue 6, 117 (2024)
Zhiwei LIU1,2, Liang SHI1,2, and Song LIU1,2
Author Affiliations
  • 1Institute of Noise and Vibration, Naval University of Engineering, Wuhan 430033, China
  • 2National Key Laboratory on Ship Vibration and Noise, Wuhan 430033, China
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    DOI: 10.19693/j.issn.1673-3185.03933 Cite this Article
    Zhiwei LIU, Liang SHI, Song LIU. Neural network-based evaluation method for alignment state of air spring vibration isolation device[J]. Chinese Journal of Ship Research, 2024, 19(6): 117 Copy Citation Text show less
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    工况号充气工况类型
    Q1根据压力均载原则充气至对中状态
    Q2循环充:每轮顺序为1→2→3→4→5→6→7→8,共8轮
    Q3先充首部:1→2→3→4充8次,后5→6→7→8充8次
    Q4先充右侧:1→3→5→7充8次,后8→6→4→2充8次
    Q5先充某一角:1→2→3→5充8次,后8→7→6→4充8次
    Q6先充前后端,后充中间:1→2→7→8充8次,3→4→5→6充8次
    Table 1. The main typical inflation conditions
    参数参数设置
    隐藏层节点数第1层:128;第2层:64;第3层:32
    学习率0.002
    迭代次数3 000
    激活函数第1,2层:sigmoid();第3层:relu( )
    损失函数torch.nn.MSELoss( )
    优化器torch.optim.Adam( )
    Table 2. Parameter settings for neural network model
    数据集误差值
    x误差z误差α误差γ误差总体误差
    测试集1(平滑处理)0.0750.3610.0180.1470.199
    测试集1(无平滑处理)0.0100.3900.0120.1600.216
    测试集2(平滑处理)0.2680.2480.0130.1180.192
    Table 3. Prediction error of neural network model for small air spring vibration isolation device
    对中不对中
    原始数据1 3731 304
    预测结果1 3221 140
    准确度/%96.2987.42
    Table 4. Prediction accuracy of the two types of data in Test Set 3
    误差种类误差值
    x 误差0.011
    z 误差0.014
    α 误差0.003
    γ 误差0.001
    总体误差0.010
    Table 5. Neural network prediction error for large air spring vibration isolation device
    Zhiwei LIU, Liang SHI, Song LIU. Neural network-based evaluation method for alignment state of air spring vibration isolation device[J]. Chinese Journal of Ship Research, 2024, 19(6): 117
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