文章摘要
基于PF能量特征和SVM的变速箱轴承故障诊断方法
Rolling bearing diagnosis based on PF energy feature and SVM
  
DOI:
中文关键词: 轴承  LMD  能量特征  支持向量机  故障诊断
英文关键词: rolling bearing  energy feature  LMD  SVM  Fault Diagnosis
基金项目:国家自然科学基金资助项目(51175169,51205124);湖南省科技厅项目(2013FJ4045)
作者单位
沈意平,贺赛坪,何宽芳,李学军 湖南科技大学 机械设备健康维护湖南省重点实验室湖南 湘潭 411201 
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中文摘要:
      内圈点蚀、外圈压痕是变速箱滚动轴承常见典型故障,为实现其快速、准确诊断,提出基于局部均值分解(Local mean decomposition,简称LMD)的PF (Product Function)分量能量特征和支持向量机(Support Vector Machine,简称SVM)相结合的变速箱滚动轴承诊断方法.将采集的振动信号进行LMD局部均值分解,获得若干个PF分量,并以计算的PF分量的能量熵作为特征量输入支持向量机,进行滚动轴承的故障类型的识别.通过对滚动轴承正常状态、内圈点蚀故障和外圈压痕故障的诊断效果对比分析表明,相对于基于神经网络的轴承故障诊断方法,基于PF分量能量特征和支持向量机的诊断方法有着更高的故障识别率.
英文摘要:
      The inner ring erosion and outer indentation are typical faults of rolling bearing. In order to diagnose these faults rapidly and accurately, a novel diagnosis method of rolling bearing was proposed based on the energy characteristics of PF (Product Function) component and support vector machine (Support Vector Machine, SVM) by the vibration signal of local mean decomposition(Local mean decomposition, LMD). The collected vibration signals were decomposed into several PF components by the local mean decomposition, the calculated energy feature of the PF component were inputted to the support vector machine to identify the type of rolling bearing faults. The results show that the method has a high diagnosis and recognition rate for the typical faults of rolling bearing.
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