集合经验模式分解在柴油机机械故障诊断中的应用  

Application of EEMD in Fault Diagnosis of Diesel Engine

作　　者：张玲玲[1,2] 骆诗定[2] 肖云魁[2] 赵懿冠[2] 廖红云[2] 

机构地区：[1]军械工程学院火炮工程系,石家庄050003 [2]军事交通学院汽车工程系,天津300161

出　　处：《科学技术与工程》2010年第27期6745-6749,共5页Science Technology and Engineering

摘　　要：针对柴油机表面振动信号非平稳、非线性等特点,引入集合经验模式分解(EEMD)的信号分析方法,对原始振动信号叠加适当的随机高斯白噪声,从而改变信号的局部时间跨度,有效抑制了经验模式分解(EMD)的模式混叠现象。通过Hilbert变换作边际谱曲线以提取故障特征信息。仿真试验和发动机故障实例证实了EEMD算法可以提高振动信号的分析精度,在柴油机机械故障诊断领域应用前景广泛。For the non-stationary and non-liner characteristics of diesel engine vibration signal,a new analytic method based on ensemble empirical mode decomposition（EEMD）is introduced.Moderate Gauss white noise generated randomly is added to the original vibration signal,which can change local time span of the signal,and the problem of mode mixing in frequency which is drawback of EMD is avoided efficiently,so that the fault characteristics of the engine can be extracted from the marginal Hilbert spectrum.The experimental results of both simulation experiment and fault diagnosis in diesel engine show that the EEMD can effectively improve vibration signal analysis precision and has good prospects of application in machinery fault diagnosis of diesel engine.

关 键 词：柴油机 机械故障诊断 集合经验模式分解 经验模式分解 边际谱曲线 

分 类 号：TK478[动力工程及工程热物理—动力机械及工程]