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基于EMD与KPCA的滚动轴承故障特征提取及诊断方法研究

徐卓飞 张海燕 王丹 张明龙

徐卓飞, 张海燕, 王丹, 张明龙. 基于EMD与KPCA的滚动轴承故障特征提取及诊断方法研究[J]. 机械科学与技术, 2014, 33(10): 1518-1524. doi: 10.13433/j.cnki.1003-8728.2014.1016
引用本文: 徐卓飞, 张海燕, 王丹, 张明龙. 基于EMD与KPCA的滚动轴承故障特征提取及诊断方法研究[J]. 机械科学与技术, 2014, 33(10): 1518-1524. doi: 10.13433/j.cnki.1003-8728.2014.1016
Xu Zhuofei, Zhang Haiyan, Wang Dan, Zhang Minglong. Study on Feature Extraction and Diagnosis Method of Rolling Bearing Faults Based on EMD and KPCA[J]. Mechanical Science and Technology for Aerospace Engineering, 2014, 33(10): 1518-1524. doi: 10.13433/j.cnki.1003-8728.2014.1016
Citation: Xu Zhuofei, Zhang Haiyan, Wang Dan, Zhang Minglong. Study on Feature Extraction and Diagnosis Method of Rolling Bearing Faults Based on EMD and KPCA[J]. Mechanical Science and Technology for Aerospace Engineering, 2014, 33(10): 1518-1524. doi: 10.13433/j.cnki.1003-8728.2014.1016

基于EMD与KPCA的滚动轴承故障特征提取及诊断方法研究

doi: 10.13433/j.cnki.1003-8728.2014.1016
基金项目: 

国家自然科学基金项目(51275406,51305340),陕西省自然科学基础研究计划项目(2013JM7009)和陕西省教育厅科学研究计划项目(2013JK1030)资助

详细信息
    作者简介:

    徐卓飞(1985-),博士研究生,研究方向为机械故障测试与诊断,xzf_34216606@163.com;张海燕(联系人),教授,hyzhang@xaut.edu.cn

    徐卓飞(1985-),博士研究生,研究方向为机械故障测试与诊断,xzf_34216606@163.com;张海燕(联系人),教授,hyzhang@xaut.edu.cn

Study on Feature Extraction and Diagnosis Method of Rolling Bearing Faults Based on EMD and KPCA

  • 摘要: 针对滚动轴承故障种类识别与程度判断问题,提出了一种融合经验模式分解与核主元分析的故障诊断方法:首先,运用经验模式分解将滚动轴承故障信号分解成不同特征尺度下的本征模式分量,采用Hilbert-Huang变换对信号进行相应的时频分析,从本征模式分量函数和瞬时频率中分别提取时域和频域的统计特征集与无量纲特征集;其次,引入非线性核主元分析方法,对故障特征集进行处理,从而消除特征集中的冗余特征,并大幅度降低特征向量维数,得到能够反映故障本质的主元特征集;最后,构造支持向量机多类分类网络,实现了对不同故障模式与不同损伤程度滚动轴承的故障诊断。
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  • 收稿日期:  2013-04-10

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