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基于多核监督流形学习的旋转机械故障诊断

杨长远 马赛 韩勤锴

杨长远, 马赛, 韩勤锴. 基于多核监督流形学习的旋转机械故障诊断[J]. 航空动力学报, 2024, 39(10):20220184 doi: 10.13224/j.cnki.jasp.20220184
引用本文: 杨长远, 马赛, 韩勤锴. 基于多核监督流形学习的旋转机械故障诊断[J]. 航空动力学报, 2024, 39(10):20220184 doi: 10.13224/j.cnki.jasp.20220184
YANG Changyuan, MA Sai, HAN Qinkai. Fault diagnosis of rotating machinery based on multi-kernel supervised manifold learning[J]. Journal of Aerospace Power, 2024, 39(10):20220184 doi: 10.13224/j.cnki.jasp.20220184
Citation: YANG Changyuan, MA Sai, HAN Qinkai. Fault diagnosis of rotating machinery based on multi-kernel supervised manifold learning[J]. Journal of Aerospace Power, 2024, 39(10):20220184 doi: 10.13224/j.cnki.jasp.20220184

基于多核监督流形学习的旋转机械故障诊断

doi: 10.13224/j.cnki.jasp.20220184
基金项目: 国家自然基金(51705275,51335006,11872222); 山东大学基本科研业务费(2019GN046); 高效洁净机械制造教育部重点实验室(山东大学)基金; 中央高校基本科研业务费专项资金(2020QNQT002); 山东省脑功能重构省级重点实验室开放基金(2021NGN003); 山东省重点研发计划(重大科技创新工程)项目(2021CXGC011105)
详细信息
    作者简介:

    杨长远(1998-),男,硕士生,主要研究方向为机械故障诊断和人工智能

    通讯作者:

    马赛(1986-),男,副研究员、硕士生导师,博士,主要研究方向为机械设备的预测性维护、机械信号处理和人工智能。E-mail:massana@163.com

  • 中图分类号: V263.6;TH17

Fault diagnosis of rotating machinery based on multi-kernel supervised manifold learning

  • 摘要:

    为了准确地对旋转机械进行故障诊断,提出了一种多核监督流形学习算法(multi-kernel supervised manifold learning,MKSML)。MKSML算法可以有效地对高维故障数据进行特征选择,筛选出区分度高的低维故障特征。借助监督学习的思想,增强了同类样本的聚集性和不同类样本之间的差异性;同时基于所设计的多核函数提出了加权邻域图构建方法,能够保留近邻点之间的距离信息和角度信息,有效地抑制故障特征选择时样本中的异常值和噪声的干扰。通过灰狼优化算法调整MKSML算法相应的参数,使算法能够应用于不同类型的旋转机械故障诊断。在此基础上,建立了一种基于MKSML算法的旋转机械故障诊断模型,并进行了轴承故障诊断实验以及齿轮故障诊断实验。

     

  • 图 1  基于MKSML算法的故障诊断模型流程图

    Figure 1.  Flow chart of fault diagnosis model based on MKSML

    图 2  分解滤波器

    Figure 2.  Analysis filter banks

    图 3  多分类器集成学习流程图

    Figure 3.  Flow chart of multi-classifier ensemble learning

    图 4  行星齿轮轴承实验装置

    Figure 4.  Planet gear bearing experimental rig

    图 5  变速箱运行状态模拟实验装置

    Figure 5.  Gearbox running state simulation experimental rig

    图 6  5种不同状态下的齿轮

    Figure 6.  Five gears in different states

    表  1  行星齿轮轴承故障诊断实验结果

    Table  1.   Experimental results of planet gear bearing fault diagnosis

    降维方法 无故障/% 滚动体故障/% 外圈故障/% 内圈故障/% 平均/%
    MKSML 100.00 100.00 100.00 100.00 100.00
    LVM 100.00 98.33 100.00 98.33 99.17
    LLE 93.33 96.67 96.67 98.33 96.25
    PCA 90.00 86.67 86.67 91.67 88.75
    SAE 91.67 96.67 93.33 91.67 93.33
    SOM 100.00 90.00 68.33 86.67 86.25
    KLDA 93.33 86.67 91.67 90.00 90.42
    MKSLLE 98.33 98.33 100.00 100.00 99.17
    下载: 导出CSV

    表  2  齿轮故障诊断实验结果

    Table  2.   Experimental results of gear fault diagnosis

    降维方法 健康/% 缺齿/% 齿根裂纹/% 剥落/% 齿尖削平/% 平均/%
    MKSML 100.00 95.00 100.00 92.50 92.50 96.00
    LVM 85.00 95.00 100.00 75.00 97.50 90.50
    LLE 100.00 87.50 100.00 80.00 100.00 93.50
    PCA 75.00 97.50 90.00 87.50 85.00 87.00
    SAE 80.00 100.00 97.50 87.50 97.50 92.50
    SOM 87.50 100.00 87.50 97.50 100.00 94.50
    KLDA 85.00 100.00 100.00 100.00 90.00 95.00
    MKSLLE 100.00 90.00 100.00 87.50 97.50 95.00
    下载: 导出CSV
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  • 收稿日期:  2022-04-02
  • 网络出版日期:  2024-05-16

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