基于EEMD和模糊C均值聚类算法诊断发动机曲轴轴承故障  

Diagnosis on Crankshaft Bearing Fault Based on EEMD and Fuzzy C Mean Clustering Arithmetic

作　　者：张玲玲[1,2] 廖红云[2] 曹亚娟[2] 骆诗定[2] 赵懿冠[2] 

机构地区：[1]军械工程学院火炮工程系,河北石家庄050003 [2]军事交通学院汽车工程系,天津300161

出　　处：《内燃机学报》2011年第4期332-336,共5页Transactions of Csice

基　　金：总装备部预研资助项目(40407030302)

摘　　要：针对发动机振动信号的非平稳性以及特征参数的模糊性特点,提出了一种基于集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)和模糊C均值聚类(Fuzzy Center Mean,FCM)的故障诊断方法,通过对已知故障样本信号进行EEMD分解,形成初始特征向量矩阵;对该矩阵进行奇异值分解,将矩阵的奇异值组成故障特征向量,标准化后作为FCM的输入,得到分类矩阵和聚类中心;最后通过计算待测故障样本与已知故障样本聚类中心的贴近度实现故障模式识别.故障诊断实例表明,该方法能有效地诊断柴油机曲轴轴承故障.For the non-stationary characteristics of diesel vibration signal and fuzzy characteristics of fea-ture parameters,a method of fault diagnosis based on EEMD and Fuzzy C Mean clustering arithmetic is proposed.By decomposing the known-fault samples with EEMD,an initial characteristic vector matrix is obtained.Using a SVD method to the initial vector matrix,these singular values compose the fault feature vector,which is used as the input of FCM after standardization.The optimized classified matrix and clus-tering centers are obtained.Calculating the nearness degree between the unknown-fault samples and the known-fault ones,the fault pattern is identified.Experimental results indicate that this method can effec-tively diagnose the faults of diesel crankshaft bearing.

关 键 词：模糊C均值聚类算法 奇异值分解 经验模式分解 故障诊断 曲轴轴承 

分 类 号：TK428[动力工程及工程热物理—动力机械及工程]