文章摘要
郭洋,钱鹏,胡韶奕,郑直.基于Autogram的齿轮断齿故障特征提取方法[J].机床与液压,2021,49(1):180-186.
GUO Yang,QIAN Peng,HU Shaoyi,ZHENG Zhi.Feature Extraction of Gearbox Broken Tooth Fault Based on Method of Autogram[J].Machine Tool & Hydraulics,2021,49(1):180-186
基于Autogram的齿轮断齿故障特征提取方法
Feature Extraction of Gearbox Broken Tooth Fault Based on Method of Autogram
  
DOI:10.3969/j.issn.1001-3881.2021.01.036
中文关键词: Autogram方法  断齿故障  谱峭度  故障诊断  特征提取
英文关键词: Autogram method  Broken tooth fault  Spectral kurtosis  Fault diagnosis  Feature extraction
基金项目:工科专业基于CDIO的多方协同育人模式改革与实践(2018GJJG614);华北理工大学轻工学院河北省一流本科专业建设重点支持项目;河北省博士后科学基金项目(B2020003033);河北省省属高等学校基本科研业务费研究项目(JQN20190004);唐山市应用基础研究计划项目(20130211b);华北理工大学博士科研启动基金项目(0088/28412499)
作者单位E-mail
郭洋 华北理工大学轻工学院 guoyang861212@163.com 
钱鹏 华北理工大学轻工学院  
胡韶奕 华北理工大学轻工学院  
郑直 华北理工大学机械工程学院惠达卫浴股份有限公司 zhengzhi@ncst.edu.cn 
摘要点击次数: 186
全文下载次数: 0
中文摘要:
      针对复杂生产背景下产生的强噪声淹没齿轮有效故障特征信息的问题,利用Autogram方法对其进行特征提取。该方法利用最大重叠离散小波包变换,对齿轮断齿故障振动信号进行不同层数分解处理,每层得到若干个信号,被称为“node”。为了更加全面地描述故障特征信息,对每个node进行包络谱的3种无偏自相关谱峭度求取,以便选取合适node作为信号源进行下一步分析。最后,对该信号源引入阈值处理,以便加强频谱分析的全面性,实现对齿轮断齿故障特征信息的有效提取。通过对比分析仿真和实测齿轮故障振动信号,验证了该方法的有效性。
英文摘要:
      Fault feature information extraction of broken tooth is often contaminated out by strong production background noises.Aiming at the problem,Autogram method was applied to extract the feature information.In the method,maximum overlapping discrete wavelet packet transform was utilized to decompose a contaminated signal of broken tooth fault into several signals,and each signal was called “node”.In order to depict the feature information comprehensively,unbiased autocorrelation of squared envelope for each node was computed,then one node was selected as the data source to be analyzed.For the sake of effective feature information extraction,threshold processing was introduced,and spectrum analysis could be more comprehensive.The method is effective through comparing the simulation signal sand the test ones.
查看全文   查看/发表评论  下载PDF阅读器
关闭

分享按钮