基于CEEMDAN和RCMDE的往复压缩机轴承故障诊断方法  

Bearing Fault Diagnosis Method for Reciprocating Compressor Based on CEEMDAN and RCMDE

作　　者：王金东[1] 欧凌非 赵海洋[1] 宋美萍[1] WANG Jindong;OU Lingfei;ZHAO Haiyang;SONG Meiping(School of Mechanical Science and Engineering,Northeast Petroleum University,Daqing Heilongjiang 163318,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

出　　处：《机床与液压》2021年第5期168-172,161,共6页Machine Tool & Hydraulics

基　　金：黑龙江省自然科学基金项目(E2016009);东北石油大学青年科学基金项目(2018ANC-31)。

摘　　要：针对往复压缩机振动加速度信号的非线性、非平稳等特性,提出一种基于自适应噪声完备集合经验模态分解(CEEMDAN)和精细复合多尺度散布熵(RCMDE)的往复压缩机轴承故障特征提取方法。采用CEEMDAN方法对信号进行分解时,通过不同的参数组合,可得到不同的IMF分量;计算不同参数条件下重构后的信号的峭度值,选用峭度值最大的一组参数重新对信号进行CEEMDAN分解,并进行信号重构。对重构后的信号进行RCMDE分析,提取故障特征向量,并利用支持向量机(SVM)进行分类识别。将优选参数的CEEMDAN分解方法和原CEEMDAN分解方法进行对比,结果表明:优选参数的CEEMDAN分解方法能更好地提取往复压缩机周期冲击性信号,有利于提高故障诊断的精确度。Aiming at the non-linear and non-stationary characteristics of the reciprocating compressor vibration acceleration signal, a reciprocating compressor bearing fault feature extraction method was proposed based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and refined composite multi-scale dispersion entropy(RCMDE). When using the CEEMDAN method to decompose the signal, through different parameter combinations, different IMF components could be obtained;the kurtosis values of the reconstructed signal were calculated under different parameters, the set of parameters with the largest kurtosis value was selected to carry out CEEMDAN decomposition for the signal again, and the signal reconstruction was carry out. The reconstructed signals were analyzed through RCMDE, the fault feature vectors were extracted, and the support vector machine(SVM) was used for classification and recognition. The CEEMDAN decomposition method with optimized parameters was compared with the original CEEMDAN decomposition method. The results show that by using the CEEMDAN decomposition method with optimized parameters, the periodic impact signal of the reciprocating compressor can be better extracted, which is beneficial to improve the accuracy of fault diagnosis.

关 键 词：自适应噪声完备集合经验模态分解 精细复合多尺度散布熵 信号重构 往复压缩机 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化]