基于改进的CEEMDAN和CHDE的往复压缩机气阀故障诊断  

Gas Valve of Reciprocating Compressor Fault Diagnosis Based on Improved CEEMDAN and CHDE

作　　者：宋美萍[1] 王金东[1] 赵海洋[1] 李艳春 SONG Meiping;WANG Jindong;ZHAO Haiyang;LI Yanchun(Heilongjiang Key Laboratory of Petroleum Mechanical Engineering,Northeast Petroleum University,Daqing Heilongjiang 163318,China;Daqing Petrochemical Company,Daqing Heilongjiang 163714,China)

机构地区：[1]东北石油大学黑龙江省石油机械工程重点实验室,黑龙江大庆163318 [2]大庆石化公司,黑龙江大庆163714

出　　处：《机床与液压》2023年第1期202-207,共6页Machine Tool & Hydraulics

基　　金：黑龙江省自然科学基金联合引导项目(LH2021E021)。

摘　　要：由于往复压缩机的振动信号具有非线性非平稳性的特点,为进一步提高故障识别率,提出对自适应噪声完备集合经验模态分解(CEEMDAN)进行改进并与复合层次散布熵相结合的往复压缩机气阀故障诊断方法。利用正交性为指标选择最佳模态函数,有效提高了CEEMDAN对非平稳性信号的分解精度,减少噪声残差;采用峭度作为评价指标对分解后的IMF分量进行筛选并重构信号,求解重构信号的复合层次散布熵,提取故障特征向量;利用支持向量机进行分类识别。试验结果验证了该方法的有效性和优越性。Since the vibration signal of the reciprocating compressor has the characteristics of non-linear and non-stationary,in order to further improve the fault recognition rate,a reciprocating compressor gas valve fault diagnosis method based on improved complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and composite hierarchical dispersion entropy was proposed.Orthogonality was used as an index to select the best modal function,by which CEEMDAN’s decomposition accuracy of non-stationary signals was effectively improved and noise residuals was reduced;kurtosis was used as an evaluation index to screen the decomposed IMF components and reconstruct the signal.The composite hierarchical dispersion entropy of the reconstructd signal was solved,the fault feature vector was extracted;the support vector machine was used for classification and recognition.The effectiveness and superiority of the method was verified by the test results.

关 键 词：CEEMDAN 复合层次散布熵 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]