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应用AR模型的多参数与多测点信息融合的故障分类

孙国富 徐玉秀

孙国富, 徐玉秀. 应用AR模型的多参数与多测点信息融合的故障分类[J]. 机械科学与技术, 2017, 36(6): 925-932. doi: 10.13433/j.cnki.1003-8728.2017.0617
引用本文: 孙国富, 徐玉秀. 应用AR模型的多参数与多测点信息融合的故障分类[J]. 机械科学与技术, 2017, 36(6): 925-932. doi: 10.13433/j.cnki.1003-8728.2017.0617
Sun Guofu, Xu Yuxiu. Fault Classification based on Multi-parameter and Multi-point Information Fusion of AR Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 925-932. doi: 10.13433/j.cnki.1003-8728.2017.0617
Citation: Sun Guofu, Xu Yuxiu. Fault Classification based on Multi-parameter and Multi-point Information Fusion of AR Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(6): 925-932. doi: 10.13433/j.cnki.1003-8728.2017.0617

应用AR模型的多参数与多测点信息融合的故障分类

doi: 10.13433/j.cnki.1003-8728.2017.0617
基金项目: 

国家重大科技成果转化项目(2060403)与天津市自然科学基金重点项目(10JCZDJC23400)资助

详细信息
    作者简介:

    孙国富(1988-),硕士,研究方向为机械振动与故障诊断,tjgydxjxxy@163.com

    通讯作者:

    徐玉秀(联系人),教授,硕士生导师,博士,xuyu@tjpu.edu.cn

Fault Classification based on Multi-parameter and Multi-point Information Fusion of AR Model

  • 摘要: 为了找到针对齿轮传动系统多类故障分类的有效方法,对行星齿轮传动系统进行故障实验,获取振动信号。采用EMD方法对该振动信号进行预处理,得到若干个IMF分量之和,对前4个有效的IMF分量分别建立AR模型,得到对应的自回归参数序列φ,进而对其分别计算关联维数、最大Lyapunov指数、样本熵这3个混沌特征参数,并将其作为辨识特征量。将不同测点对应的φ的不同混沌特征参数信息融合作为支持向量机的输入向量,建立6种不同故障状态的训练集,实现对故障类型进行分类。结果表明:对实验获取的振动信号进行EMD和AR模型处理后,能在很大程度上提高故障分类准确率。
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出版历程
  • 收稿日期:  2015-06-29
  • 刊出日期:  2017-06-05

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