基于MED和边际谱的自动倾斜器轴承故障诊断

针对直升机自动倾斜器轴承早期微弱故障特征易被强烈背景噪声淹没的问题，提出了一种基于最小熵反褶积（Minimum Entropy Deconvolution，MED）和边际谱的自动倾斜器轴承故障诊断方法。采用MED对采集的振动信号进行滤波降噪，提高了信号的信噪比，突出了轴承早期微弱故障特征；通过Hilbert变换和经验模态分解（Empirical Mode Decomposition，EMD）获取去噪包络信号的本征模态函数（Intrinsic Mode Functions，IMF）集，并引入峭度筛选准则选取合理IMF集计算局部Hilbert边际谱，有效地提取了故障特征频率，能够通过故障特征频率进行故障类型判别。通过某型直升机自动倾斜器故障诊断试验系统验证了该诊断方法的合理性和可行性。

Fault Diagnosis Method of Swash-Plate Bearing Based on MED and Marginal Spectrum

Aiming to solve the problem that the early weak fault features of the helicopter swash-plate bearing are easily overwhelmed by strong background noise,a fault diagnosis method for the swash-plate bearings based on minimum entropy deconvolution (MED) and marginal spectrum is proposed.MED was used to filter and de-noise the acquired vibration signals,which improved the signal-to-noise ratio of vibration signals and underlines the early weak fault features of swash-plate bearing.Through Hilbert transform and empirical mode decomposition (EMD),the intrinsic mode functions (IMF) set from de-noised envelope signals was obtained,and the kurtosis screening criteria was introduced to select a reasonable IMF set to calculate the local Hilbert marginal spectrums.The fault characteristic frequency was effectively extracted,and the fault type through it can be judged.The swash-plate bearing fault diagnostic test system of one type helicopter verifies the rationality and feasibility of the diagnosis method.