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Adaboost_SVM集成模型的滚动轴承早期故障诊断

陈法法 杨晶晶 肖文荣 程珩 张发军

陈法法, 杨晶晶, 肖文荣, 程珩, 张发军. Adaboost_SVM集成模型的滚动轴承早期故障诊断[J]. 机械科学与技术, 2018, 37(2): 237-243. doi: 10.13433/j.cnki.1003-8728.2018.0212
引用本文: 陈法法, 杨晶晶, 肖文荣, 程珩, 张发军. Adaboost_SVM集成模型的滚动轴承早期故障诊断[J]. 机械科学与技术, 2018, 37(2): 237-243. doi: 10.13433/j.cnki.1003-8728.2018.0212
Chen Fafa, Yang Jingjing, Xiao Wenrong, Cheng Hang, Zhang Fajun. Early Fault Diagnosis of Rolling Bearing based on Ensemble Model of Adaboost SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 237-243. doi: 10.13433/j.cnki.1003-8728.2018.0212
Citation: Chen Fafa, Yang Jingjing, Xiao Wenrong, Cheng Hang, Zhang Fajun. Early Fault Diagnosis of Rolling Bearing based on Ensemble Model of Adaboost SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(2): 237-243. doi: 10.13433/j.cnki.1003-8728.2018.0212

Adaboost_SVM集成模型的滚动轴承早期故障诊断

doi: 10.13433/j.cnki.1003-8728.2018.0212
基金项目: 

国家自然科学基金项目(51405264,51475266)与湖北省重点实验室开放基金项目(2017KJX02)资助

详细信息
    作者简介:

    陈法法(1983-),副教授,硕士生导师,博士,研究方向机电装备动态测试与故障诊断,chenfafa2005@126.com

    通讯作者:

    肖文荣,讲师,博士,xiaowr@stu.xjtu.edu.cn

Early Fault Diagnosis of Rolling Bearing based on Ensemble Model of Adaboost SVM

  • 摘要: 针对滚动轴承早期故障诊断中故障特征微弱难以有效检测的问题,提出一种基于Adaboost提升支持向量机(Support vector machines,SVM)集成学习模型的滚动轴承早期故障诊断方法。首先以Cincinnati大学实测的滚动轴承全寿命振动数据为基础,采用特征参数跟踪法,建立特征参数的趋势分析,并据此选择用于滚动轴承早期故障诊断的敏感特征参量,然后通过构造Adaboost提升SVM集成学习模型,并将其应用于滚动轴承的早期故障检测中。AdaBoost能够自适应地提升单一SVM的分类性能,相对于传统的单一SVM分类器Adaboost_SVM稳定性最好,早期故障诊断准确率最高。实验结果表明,结合优选的敏感特征参量,Adaboost_SVM能有效地诊断滚动轴承的早期故障模式。
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出版历程
  • 收稿日期:  2017-02-01
  • 刊出日期:  2018-02-25

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