欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
提出了一种基于掩蔽经验模式分解（MEMD）互近似熵及模糊C均值聚类（FCM）的滚动轴承故障诊断新方法。MEMD可以有效抑制经验模式分解存在的模态混叠问题;互近似熵是近似熵的改进,能更好体现信号的不规则度和复杂度。信号经掩蔽经验模式分解后得到一组平稳的本征模函数（IMF）,通过能量分析筛选出与原始信号最为相关的几个IMF分量,计算其互近似熵值以作为故障特征向量,能够直观体现设备的运行状况。故障模式识别采用的FCM算法,计算相对简单,聚类效果好。实验分析证明了该方法的优越性。
英文摘要：
A new iault diagnosis method for rolling bearings was presented based on MEMD cAp- En and FCM clustering herein. The MEMD method could restrain mode mixing of EMD effectively and the cApEn was the improvement of approximate entropy, which could express the more irregularity and complexity. Signals were decomposed by MEMD to obtain a set of stationary intrinsic mode function （IMF）, and some IMF components that were most relevant to the original signals were sifted out by the energy analysis criterion. The cApEn values of every IMF components were calculated as fault feature vectors that could represent the operating conditions of equipment more intuitively. FCM algorithm was introduced to fault recognition, which could achieve well effect of clustering with easier calculation. The experiments and engineering analyses demonstrate the superiority of this method.
同期刊论文项目
同项目期刊论文
期刊信息
位置：