基于CIELMD与RCMFE的往复压缩机轴承间隙故障特征提取方法  

Fault Feature Extraction Method for Bearing Clearance of Reciprocating Compressor Based on CIELMD and RCMFE

作　　者：陈桂娟[1] 江群 李玉倩[2] 赵海洋[1] 王金东[1] CHEN Guijuan;JIANG Qun;LI Yuqian;ZHAO Haiyang;WANG Jindong(School of Mechanical Science and Engineering,Northeast Petroleum University,Daqing Heilongjiang 163318,China;Daqing Petrochemical Company Water and Gas Plant Sewage Joint Workshop,Daqing Heilongjiang 163714,China)

机构地区：[1]东北石油大学机械科学与工程学院,黑龙江大庆163318 [2]大庆石化公司水气厂污水联合车间,黑龙江大庆163714

出　　处：《机床与液压》2021年第15期180-187,共8页Machine Tool & Hydraulics

基　　金：东北石油大学青年科学基金资助项目(2018ANC-31)。

摘　　要：针对往复压缩机轴承间隙故障诊断振动信号强非平稳、非线性与特征耦合特性,提出基于复合插值包络局部均值分解(CIELMD)与精细复合多尺度模糊熵(RCMFE)特征提取方法。使用CIELMD方法分解不同轴承间隙故障信号,利用相关系数筛选包含主要故障信息的PF分量;通过RCMFE方法定量描述PF分量构成状态特征矩阵,为解决信息冗余问题,进一步使用文化基因算法优选矩阵中平均样本距离最大的元素,构成可分性良好的特征向量。往复压缩机轴承间隙故障模拟信号试验结果表明:该方法提取故障特征可分性强,故障识别准确率高。According to the strong nonstationarity,nonlinearity,and multi⁃component coupling characteristics of reciprocating compressor vibration signal,a feature extraction method based on compound interpolation local mean decomposition method(CIELMD)and refined composite multi⁃scale fuzzy entropy(RCMFE)was proposed.The CIELMD method was used to decompose the fault signals of different bearing clearances,and the highlighted PF components which contained the main information of fault state were chosen with the correlation coefficient.The PF components were quantitatively described by the RCMFE method to form the state characteristic matrix.Furthermore,to solve the problem of information redundancy,the memetic algorithm was further used to optimize the elements with the largest average sample distance in the matrix to form a feature vector with striking separability.Finally,the exper⁃imental results of the reciprocating compressor bearing clearance fault signal shows that the method has strong separability in extracting fault features and high fault identification accuracy.

关 键 词：复合插值包络局部均值分解 精细复合多尺度模糊熵 特征提取 故障诊断 轴承间隙 

分 类 号：TH133.3[机械工程—机械制造及自动化]