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基于EEMD-Hilbert和FWA-SVM的滚动轴承故障诊断方法

张敏 蔡振宇 包珊珊

张敏, 蔡振宇, 包珊珊. 基于EEMD-Hilbert和FWA-SVM的滚动轴承故障诊断方法[J]. 西南交通大学学报, 2019, 54(3): 633-639, 662. doi: 10.3969/j.issn.0258-2724.20170435
引用本文: 张敏, 蔡振宇, 包珊珊. 基于EEMD-Hilbert和FWA-SVM的滚动轴承故障诊断方法[J]. 西南交通大学学报, 2019, 54(3): 633-639, 662. doi: 10.3969/j.issn.0258-2724.20170435
ZHANG Min, CAI Zhenyu, BAO Shanshan. Fault Diagnosis of Rolling Bearing Based on EEMD-Hilbert and FWA-SVM[J]. Journal of Southwest Jiaotong University, 2019, 54(3): 633-639, 662. doi: 10.3969/j.issn.0258-2724.20170435
Citation: ZHANG Min, CAI Zhenyu, BAO Shanshan. Fault Diagnosis of Rolling Bearing Based on EEMD-Hilbert and FWA-SVM[J]. Journal of Southwest Jiaotong University, 2019, 54(3): 633-639, 662. doi: 10.3969/j.issn.0258-2724.20170435

基于EEMD-Hilbert和FWA-SVM的滚动轴承故障诊断方法

doi: 10.3969/j.issn.0258-2724.20170435
基金项目: 中央高校基本科研业务费专项资金资助项目(2682016CX031)、国家自然科学基金项目(51675450)
详细信息
    作者简介:

    张敏(1986—),女,博士,讲师,研究生导师,研究方向为智能故障诊断,E-mail:zhmzhangmin16@163.com

  • 中图分类号: TH17

Fault Diagnosis of Rolling Bearing Based on EEMD-Hilbert and FWA-SVM

  • 摘要: 为有效提取非平稳特性的滚动轴承振动信号特征,提高故障诊断效率,提出一种采用集合经验模态分解(empiricalmode decomposition,EEMD)、Hilbert变换的特征提取方法,并利用烟花算法优化支持向量机(support vector machine,SVM)分类参数的滚动轴承故障诊断方法. 通过EEMD方法将目标信号分解成若干个模态函数,采取Hilbert变换获取模态函数的瞬时频率,并对模态函数及其瞬时频率进行统计特征提取,从而实现特征的有效降维. 结果表明:信号经过EEMD-Hilbert处理后特征能有效提取,将训练集和测试集各600组数据代入烟花算法优化SVM模型得到测试集正确率为99.63%;比传统的遗传算法和粒子群算法优化模型分别提高0.4%和0.2%左右;同时收敛时间更短,验证了该算法模型的可行性与有效性.

     

  • 图 1  故障诊断流程

    Figure 1.  Flowchart for troubleshooting

    图 3  两种方法首个IMF

    Figure 3.  Two methods for the first IMF chart

    图 4  3种算法SVM参数迭代对比

    Figure 4.  Comparison of SVM parameters for the three algorithms

    图 5  3种分类结果对比

    Figure 5.  Comparison of the three classification results

    表  1  轴承故障样本

    Table  1.   Bearing failure samples

    轴承状态故障点直径/mm训练集测试集
    正常6060
    内圈故障0.1786060
    滚动体故障0.1786060
    外圈故障0.1786060
    内圈故障0.3566060
    滚动体故障0.3566060
    外圈故障0.3566060
    内圈故障0.5336060
    滚动体故障0.5336060
    外圈故障0.5336060
    下载: 导出CSV

    表  2  3种分类结果

    Table  2.   Classification results for the three methods

    模型迭代时间/s正确率/%
    EEMD_H模型282.999.43
    EEMD模型149.894.17
    EMD_H模型304.395.10
    下载: 导出CSV

    表  3  FWA、PSO、GA对SVM参数寻优

    Table  3.   FWA,PSO,and GA for SVM parameter optimisation

    算法C$\sigma $正确率/%
    PSO38.129.3598.67
    FWA35.396.8899.00
    GA57.8623.9798.67
    下载: 导出CSV

    表  4  3种分类结果

    Table  4.   Classification results for the three algorithms

    模型迭代时间/s正确率/%
    FWA14.499.63
    GA114.399.20
    PSO282.999.43
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-14
  • 修回日期:  2017-10-16
  • 网络出版日期:  2019-02-22
  • 刊出日期:  2019-06-01

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