摘要
轴承运行时的振动信号是典型的非线性非平稳时间序列,对其建立时变自回归参数模型,可以较好地表征轴承振动的非平稳特征。在对轴承振动信号时变自回归模型的时变参数进行大量实验分析研究的基础上,提取均值作为表征轴承运行状态的特征参数,并输入支持向量机分类器进行故障识别与分类,实现滚动轴承的智能故障诊断。实验结果表明,该故障诊断方法可以有效准确地识别滚动轴承的运行状态。
The vibration signals of a bearing are typical nonlinear and non-stationary time series,and the non-stationary can be preferably characterized by establishing their time-varying autoregressive（TVAR）model.After adopting large numbers of experimental analysis to the parameters of the TVAR of the vibration signals,the means of time-varying autoregressive parameters can be extracted as the feature vectors of the bearing’s run state,and were input to support vector machine（SVM）classifier to recognize and classify the fault patterns,then an intelligent fault diagnosis was realized.The experimental results show the effectiveness and accuracy of the proposed approach for recognizing the states of rolling bearings.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2010年第22期2657-2661,2704,共6页
China Mechanical Engineering
基金
国家自然科学基金资助项目(60774069)
中国博士后科学基金资助项目(20070410462)
省部级重点基金资助项目(9140A17051010BQ0104)
湖南省教育厅科技计划项目(07C005)
关键词
故障诊断
时变自回归参数模型
特征提取
滚动轴承
fault diagnosis
time-varying autoregressive model
feature extract
rolling bearing
