摘要
针对滚动轴承发生故障时呈现出的循环平稳特征,提出基于二阶循环平稳的谱相关密度组合切片能量熵-连续隐马尔可夫(SEESCD-CHMM)的滚动轴承故障诊断方法.首先用SEESCD分析方法对滚动轴承4种工作状态(正常、内圈故障、外圈故障和滚动体故障)的数据进行特征提取组成训练特征向量;然后用训练特征向量对CHMM进行模型训练,取得CHMM模型的最优参数;最后用谱SEESCD分析方法对测试数据进行特征提取得到测试特征向量,用训练好的隐马尔科夫(HMM)模型对测试特征向量进行诊断,取得了准确率较高的诊断结果.
Taking advantage of the property of cyclostationarity when fault arises in rolling bearing,the paper proposes the fault diagnosis method of rolling bearing based on slice energy entropy-continuous hidden Markov model (SEESCD-CHMM).Firstly,use the method of SEESCD to extract the rolling bearing four states’(normal,inner race fault,outer race fault and ball element fault) data to form the training feature vectors.Then use the training feature vectors to train the model of CHMM and get the optimal parameters of CHMM.At last,use the SEESCD to extract the test data to form the test feature vectors.Use the trained CHMM model to diagnose the test feature vectors and perfect diagnosis results are got.
作者
郝芳
王宏超
李宏伟
HAO Fang;WANG Hongchao;LI Hongwei(Department of Information Engineering,Huanghe Science and Technology College,Zhengzhou 450063,China;Mechanical and Electrical Engineering Institute,Zhengzhou University of Light Industry,Zhengzhou 450002,China)
出处
《中国工程机械学报》
北大核心
2019年第2期184-188,共5页
Chinese Journal of Construction Machinery
