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基于特征迁移学习的提升机轴承智能故障诊断

潘晓博 葛鲲鹏 董飞

潘晓博,葛鲲鹏,董飞. 基于特征迁移学习的提升机轴承智能故障诊断[J]. 工矿自动化,2022,48(9):1-7, 32.  doi: 10.13272/j.issn.1671-251x.17980
引用本文: 潘晓博,葛鲲鹏,董飞. 基于特征迁移学习的提升机轴承智能故障诊断[J]. 工矿自动化,2022,48(9):1-7, 32.  doi: 10.13272/j.issn.1671-251x.17980
PAN Xiaobo, GE Kunpeng, DONG Fei. Intelligent fault diagnosis of hoist bearing based on feature transfer learning[J]. Journal of Mine Automation,2022,48(9):1-7, 32.  doi: 10.13272/j.issn.1671-251x.17980
Citation: PAN Xiaobo, GE Kunpeng, DONG Fei. Intelligent fault diagnosis of hoist bearing based on feature transfer learning[J]. Journal of Mine Automation,2022,48(9):1-7, 32.  doi: 10.13272/j.issn.1671-251x.17980

基于特征迁移学习的提升机轴承智能故障诊断

doi: 10.13272/j.issn.1671-251x.17980
基金项目: 江苏省建设系统科技项目(2018ZD077)。
详细信息
    作者简介:

    潘晓博(1980—),男,江苏徐州人,讲师,硕士,主要研究方向为物联网、嵌入式系统设计和信息采集与处理,E-mail:xiaobopan@163.com

    通讯作者:

    葛鲲鹏(1992—),男,江苏仪征人,工程师,硕士,主要研究方向为矿山物联网、信号处理和人工智能,E-mail:1103394281@qq.com

  • 中图分类号: TD633

Intelligent fault diagnosis of hoist bearing based on feature transfer learning

  • 摘要: 针对提升机复杂实际工况导致的现有故障诊断方法准确率低和适应性弱的问题,提出了一种基于深度迁移特征选取(DTF)与平衡分布自适应(BDA)的提升机轴承智能故障诊断方法。对不同工况下的轴承故障信号进行时频分析,提取时域、频域统计特征,采用深度置信网络进行高维深度特征提取。为从高维深度特征集中选取出既有利于故障模式识别,也有利于跨域故障诊断的特征,采用基于ReliefF与域间差异的迁移特征选取(TFRD)方法对各特征的可迁移性进行量化评估,利用TFRD方法对各特征进行类别区分度和域不变性量化评估,采用ReliefF算法处理各类特征数据,获得表征类别区分度的权重值;计算同一特征在不同域间的最大均值差异,构建一种新的特征可迁移性量化指标。基于TFRD 方法,选取特征可迁移性大的深度特征构建特征子集,利用BDA对源域和目标域的特征子集进行分布适应,降低两者间的分布差异。采用源域特征集训练故障模式识别分类器,对目标域样本进行故障识别与分类。采用经典机器学习方法、深度学习方法和迁移学习方法构建了8种故障诊断模型,用于与提出的DTF−BDA故障诊断模型进行故障诊断准确率对比。结果表明:① DTF−BDA故障诊断模型能够取得明显优于其他对比模型的性能,最高故障诊断准确率可达100%。② TFRD方法能有效提高基于迁移学习方法构建的故障诊断模型的性能,与迁移成分分析和联合分布自适应相结合情况下的最高故障诊断准确率分别可达96.46%和97.67%。

     

  • 图  1  基于 DTF−BDA 的提升机轴承智能故障诊断流程

    Figure  1.  Flow of hoist bearing intelligent fault diagnosis based on deep transferable feature selection and balance distribution adaptation

    图  2  基于3层RBM构建的DBN

    Figure  2.  Deep belief network based on three layer restricted Boltzmann machine

    图  3  凯斯西储大学轴承故障实验台

    Figure  3.  Bearing fault test rig of Case Western Reserve University

    表  1  凯斯西储大学轴承故障数据集

    Table  1.   Bearing fault dataset of Case Western Reserve University

    轴承状态缺陷尺
    寸/cm
    不同轴承工况下的样本数类别标签
    域1域2域3域4
    正常状态0606060601
    滚动体缺陷0.017 78606060602
    0.035 56606060603
    0.053 34606060604
    0.071 12606060605
    内圈缺陷0.017 78606060606
    0.035 56606060607
    0.053 34606060608
    0.071 12606060609
    外圈缺陷0.017 786060606010
    0.035 566060606011
    0.053 346060606012
    下载: 导出CSV

    表  2  不同故障诊断模型在4个任务下的故障诊断准确率对比

    Table  2.   Comparison of fault diagnosis accuracy of different fault diagnosis models under 4 fault diagnosis tasks %

    故障模型故障诊断准确率
    任务1任务2任务3任务4
    FS−SVM95.0073.1387.5078.96
    FS−KNN96.8882.5090.1385.00
    FS−DBN−Softmax85.2185.6382.5080.13
    FS−DAE−Softmax59.1753.9653.3351.67
    FS−TCA−SVM77.5078.7572.6776.13
    FS−JDA−SVM83.3381.6779.1777.50
    FS−TFRD−TCA96.8896.6795.4295.00
    FS−TFRD−JDA98.1398.9697.7197.08
    DTF−BDA100.00100.00100.00100.00
    下载: 导出CSV

    表  3  不同故障诊断模型实验结果

    Table  3.   Experimental results of different fault diagnosis models

    可迁移特征
    选取数
    故障诊断准确率/%
    FS−TFRD−TCAFS−TFRD−JDADTF−BDA
    任务1任务2任务3任务4任务1任务2任务3任务4任务1任务2任务3任务4
    2066.0467.0860.6763.1768.6765.8361.6760.5071.3369.8370.3368.17
    4071.6773.1366.8368.5074.5073.1772.5071.3377.5076.6775.0074.83
    6079.7981.8876.0078.6780.0081.6780.3379.2983.3382.6782.6782.50
    8083.5486.0482.6784.5089.8387.0087.5088.1391.6792.0092.5093.13
    10088.9688.3384.8386.5095.1796.8394.8394.5099.5099.1799.6799.50
    12095.0095.6391.3389.1797.6796.0097.5096.3398.3398.1799.1398.67
    14096.4696.0096.3395.5095.6795.1794.8394.3396.6796.5097.6796.46
    16089.3892.6786.5084.8388.0086.8389.3387.0094.8393.3395.5094.83
    18080.8382.5076.3382.6784.3382.5086.1784.6792.1791.5093.1392.67
    20077.5078.7572.6776.1383.3381.6779.1777.5088.7586.4683.7582.08
    下载: 导出CSV
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  • 收稿日期:  2022-07-06
  • 修回日期:  2022-08-29
  • 网络出版日期:  2022-09-24

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