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模糊粒化非监督学习结合随机森林融合的旋转机械故障诊断

温江涛 周熙楠

温江涛, 周熙楠. 模糊粒化非监督学习结合随机森林融合的旋转机械故障诊断[J]. 机械科学与技术, 2018, 37(11): 1722-1730. doi: 10.13433/j.cnki.1003-8728.20180069
引用本文: 温江涛, 周熙楠. 模糊粒化非监督学习结合随机森林融合的旋转机械故障诊断[J]. 机械科学与技术, 2018, 37(11): 1722-1730. doi: 10.13433/j.cnki.1003-8728.20180069
Wen Jiangtao, Zhou Xi'nan. Fault Diagnosis of Rotating Machinery in Combination with Unsupervised Learning of Fuzzy Granulation and Random Forest Fusion[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1722-1730. doi: 10.13433/j.cnki.1003-8728.20180069
Citation: Wen Jiangtao, Zhou Xi'nan. Fault Diagnosis of Rotating Machinery in Combination with Unsupervised Learning of Fuzzy Granulation and Random Forest Fusion[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1722-1730. doi: 10.13433/j.cnki.1003-8728.20180069

模糊粒化非监督学习结合随机森林融合的旋转机械故障诊断

doi: 10.13433/j.cnki.1003-8728.20180069
基金项目: 

国家自然科学基金项目(51204145)与河北省自然科学基金项目(E2016203223,E2013203300)资助

详细信息
    作者简介:

    温江涛(1974-),副教授,博士,研究方向为振动信号分析与处理,无线传感网络,wens2002@163.com

Fault Diagnosis of Rotating Machinery in Combination with Unsupervised Learning of Fuzzy Granulation and Random Forest Fusion

  • 摘要: 在旋转机械的智能故障诊断中,复杂网络结构的非监督学习方法调节参数多,训练时间长,而结构简单的网络诊断准确率不够理想。针对以上问题,采用模糊信息粒化和稀疏自编码器搭建并行结构的学习网络,并行结构的稀疏自编码器同时对粒化后重新构成的多个有效参量信息自适应的进行特征提取,随后使用随机森林方法对提取的特征进行融合分类。实验结果表明该方法可以有效实现高精度故障诊断;且与常用的串行多网络处理结构相比,降低了网络参数调节的复杂度和多层网络的前后影响,并且提高了诊断精度,减少了训练时间。
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出版历程
  • 收稿日期:  2017-10-15
  • 刊出日期:  2018-11-05

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