基于EMD和Gabor变换的发动机曲轴轴承故障特征提取  

Fault Feature Extraction of Engine Crankshaft Bearing Based on EMD and Gabor Transform

作　　者：沈虹[1,2] 赵红东[1] 张玲玲[3] 肖云魁[3] 赵慧敏[3] 

机构地区：[1]河北工业大学信息工程学院,天津300401 [2]军事交通学院基础部,天津300161 [3]军事交通学院汽车工程系,天津300161

出　　处：《汽车工程》2014年第12期1546-1550,共5页Automotive Engineering

基　　金：总装备部预研课题项目(ZLY2011601)资助

摘　　要：针对发动机振动信号的非平稳特点,提出了一种基于经验模态分解(EMD)和Gabor变换相结合的曲轴轴承故障特征提取新方法。通过EMD方法将发动机非稳态加速振动信号分解成多个本征模态函数(IMF),对与原信号相关性强的前4阶IMF分量进行Gabor变换,从各阶分量Gabor时频分布图的频带能量累加曲线中提取能够反映曲轴轴承磨损故障的频带能量作为故障特征参数。试验结果表明,该方法提取的故障特征参数能敏感地反映曲轴轴承的磨损状态,可作为诊断曲轴轴承故障的重要特征量。In view of the instability feature of engine vibration signals,a method based on the combination of empirical mode decomposition（ EMD） and Gabor transform is proposed to extract the fault features of crankshaft bearing. Firstly by using EMD technique the unstable acceleration vibration signals of engine are decomposed into a series of intrinsic mode functions（ IMFs）. Then Gabor transform is performed on the first 4 orders of IMF components having strong correlation with origin signals. Finally the frequency band energy,which well reflects the wear fault of crankshaft bearing,is extracted as fault characteristic parameter from the frequency band energy accumulation curve of Gabor time / frequency distribution graph for each IMF component. The test results indicate that the fault characteristic parameter extracted with the method proposed can sensitively reflect the wear states of crankshaft bearing and can be taken as the important characteristic quantity for the diagnosis of crankshaft bearing faults.

关 键 词：曲轴轴承 故障诊断 经验模式分解 GABOR变换 

分 类 号：U472.43[机械工程—车辆工程] U464.133.3[交通运输工程—载运工具运用工程]