流体机械2024，Vol.52Issue(11)：72-78,7.DOI:10.3969/j.issn.1005-0329.2024.11.010
基于MOMEDA与LMD的往复压缩机活塞杆沉降信号故障特征提取方法研究
Research on fault feature extraction method for reciprocating compressor piston rod settlement signal based on MOMEDA and LMD
摘要
Abstract
In response to the shortage of insufficient reliability of traditional empirical mode decomposition(EMD)methods in the current fault diagnosis of reciprocating compressor piston rods,this paper obtains the settlement signal of reciprocating compressor piston rods through eddy current sensors,uses multipoint optimal minimum entropy deconvolution algorithm(MOMEDA)to adaptively adjust the signal period to eliminate interference,and then performs local mean decomposition(LMD)on it to obtain the characteristic parameter factors of multiple product function(PF)components corresponding to the signal,including skewness coefficient gi,kurtosis coefficient qi,and total energy ratio Ei/E.Comparing the changes in characteristic parameters of the piston rod under normal and faulty conditions(support ring wear,loose fastening components,and early cracks),the results show that under the condition of piston rod support ring wear,the values of g1 and q3 will reach around-0.02 and 1.60,respectively,which is 3～5 times different from the normal values;When the fastening components of the piston rod are loose,g1,g3,q1,q3 will all show significant deviations,even exceeding the normal value by more than 10 times;In the case of early cracks in the piston rod,there will be some changes in the low order components g4 and q4,reaching around-1.30 and 1.60,respectively;The combination of MOMEDA and LMD method can accurately and effectively judge the settlement signal of reciprocating compressor piston rod.Compared with the traditional EMD signal analysis method,this method has shown higher practicality in the field of piston rod fault diagnosis.关键词
多点最优最小熵解卷积算法/局部均值分解/经验模态分解/故障诊断/往复压缩机/活塞杆Key words
MOMEDA/LMD/EMD/fault diagnosis/reciprocating compressor/piston rod分类
机械制造引用本文复制引用
何明,方燚,孙瑞亮,李豪,刘世成,范文俊,闫慧敏,舒悦..基于MOMEDA与LMD的往复压缩机活塞杆沉降信号故障特征提取方法研究[J].流体机械,2024,52(11):72-78,7.基金项目
国家重点研发计划项目(2022YFB4003404) （2022YFB4003404）
