中文摘要：

Hilbert-Huang变换（Hilbert-Huang transform,HHT）通过经验模式分解（Empirical mode decomposition,EMD）和Hilbert变换能够自适应地将复杂的非线性、非平稳信号刻画成Hilbert-Huang谱,突显信号的局部特征,具有良好的时频聚集能力,因此被广泛用于机械信号处理与故障诊断。然而,EMD存在的模式混淆问题使其难以获得准确的本征模式分量（Intrinsic mode function,IMF）。此外,通常只有部分IMF包含故障敏感信息、表征故障特征。因此基于EMD和所有IMF的Hilbert-Huang谱的故障诊断精度有待提高。为此提出一种基于总体平均经验模式分解（Ensemble empirical mode decomposition,EEMD）和敏感IMF的改进HHT。该方法利用EEMD获取无模式混淆的IMF,通过敏感度评估算法从EEMD所有的IMF中选择反应故障特征的敏感IMF,从而得到改进的Hilbert-Huang谱以更准确地诊断机械故障。通过仿真试验以及转子早期碰摩故障诊断的工程实例验证了改进HHT的有效性。

英文摘要：

Complex nonstationary and nonlinear signals can be adaptively analyzed by the Hilbert-Huang transform（HHT） through empirical mode decomposition（EMD） and the Hilbert transform to generate the Hilbert-Huang spectrum.The Hilbert-Huang spectrum is able to show the local characteristics of the signals and focus time-frequency information well,and therefore it is widely applied to analyzing vibration signals in the field of machinery fault diagnosis.EMD,however,sometimes cannot reveal the signal characteristics accurately because of the problem of mode confusion.Moreover,the extracted intrinsic mode functions（IMFs） have different sensitivity to faults.Some IMFs are sensitive and closely related to the faults but others are irrelevant.Thus it is necessary to enhance the fault diagnosis accuracy of the HHT based on all IMFs of EMD.Aiming at this problem,an improved HHT based on ensemble empirical mode decomposition（EEMD） and sensitive IMFs is proposed,in which EEMD is used to alleviate the problem of mode confusion,and the sensitivity evaluation algorithm to select sensitive IMFs and highlight potential fault characteristics.Thus,the fault diagnosis results obtained by the proposed method are more accurate than the HHT based on all IMFs of EMD.Both simulated signals and real signals of an early rub-impact fault verify the effectiveness of the improved HHT.