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基于数据挖掘的齿轮副磨损状态评估方法

张怀亮 刘森 邹佰文

张怀亮, 刘森, 邹佰文. 基于数据挖掘的齿轮副磨损状态评估方法[J]. 西南交通大学学报, 2015, 28(4): 710-716. doi: 10.3969/j.issn.0258-2724.2015.04.021
引用本文: 张怀亮, 刘森, 邹佰文. 基于数据挖掘的齿轮副磨损状态评估方法[J]. 西南交通大学学报, 2015, 28(4): 710-716. doi: 10.3969/j.issn.0258-2724.2015.04.021
ZHANG Huailiang, LIU Sen, ZOU Baiwen. Assessment Method of Gear Wear Condition Based on Data Mining[J]. Journal of Southwest Jiaotong University, 2015, 28(4): 710-716. doi: 10.3969/j.issn.0258-2724.2015.04.021
Citation: ZHANG Huailiang, LIU Sen, ZOU Baiwen. Assessment Method of Gear Wear Condition Based on Data Mining[J]. Journal of Southwest Jiaotong University, 2015, 28(4): 710-716. doi: 10.3969/j.issn.0258-2724.2015.04.021

基于数据挖掘的齿轮副磨损状态评估方法

doi: 10.3969/j.issn.0258-2724.2015.04.021
基金项目: 

国家863计划资助项目(2014AA041602)

详细信息
    作者简介:

    张怀亮(1964-),男,教授,博士生导师,研究方向为摩擦学及故障诊断,电话:0731-88876810,E-mail:zhl2001@csu.edu.cn

Assessment Method of Gear Wear Condition Based on Data Mining

  • 摘要: 为了提高齿轮副磨损状态评估的准确率,基于数据挖掘技术提出了一种新的齿轮副磨损状态评估方法.该方法通过设计直齿圆柱齿轮副磨损实验,提取实验齿轮副全寿命周期内的油液参数和振动参数,对齿轮副磨损状态进行聚类划分,建立了监测参数与齿轮副磨损状态之间的关联规则集及齿轮副磨损状态关联规则匹配算法,用于识别齿轮副的磨损状态.研究结果表明:基于数据挖掘的齿轮副磨损状态评估方法对齿轮副磨损状态的识别率达90%,能有效地评估齿轮副磨损状态.

     

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出版历程
  • 收稿日期:  2014-09-06
  • 刊出日期:  2015-08-25

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