Abstract: According to the instantaneous nonlinear and non-stationary characteristics of the vibration signals from reciprocating machine with fault，a novel adaptive denoising method based on Ensemble Empirical Mode Decomposition（EEMD） and zero-crossing detection is proposed and combined with energy moment and Support Vector Machine（SVM） to apply in fault diagnosis.With the method of EEMD，the non-stationary vibration signals are adaptively decomposed into a finite number of Intrinsic Mode Function（IMF），which can alleviate model mixing that may appear in conventional EMD method.It calculates the zero-crossing ratio of every IMF components and compares them to the predetermined threshold value，the IMF components which are satisfied for request of threshold value are obtained.That means make zero-crossing rate serve as the criterion to separate desirable components from jamming ones.So the denoised signal is obtained through reconstructing desirable IMF components.Otherwise，the energy moments of desirable IMF components are extracted as the input vector of Binary Tree Support Vector Machine（BTSVM） to realize the fault diagnosis of diesel engine，which validates the effectiveness of the method.

Key words: reciprocating machinery, signal denoising, fault feature extraction, zero-crossing rate, Ensemble Empirical Mode Decomposition（EEMD）, energy moment

摘要： 针对往复机械振动信号的瞬时非线性、非平稳特性，提出一种基于总体平均经验模式分解（Ensemble Empirical Mode Decomposition，EEMD）与过零率分析相结合的自适应降噪方法，并与能量矩、支持向量机（Support Vector Machine，SVM）结合应用于故障诊断。利用EEMD对非平稳振动信号进行自适应的分解，有效抑制经典经验模式分解的可能出现的模式混叠现象，再以所得的各固有模式分量（Intrinsic Mode Function，IMF）的过零率作为噪声评判准则，重构过零率阈值范围内的非噪声分量以实现信号降噪。另外，计算非噪声分量的能量矩作为故障特征提输入二叉树支持向量机实现的柴油机故障诊断验证了该方法有效性。

关键词: 往复机械, 信号降噪, 特征提取, 过零率分析, 总体平均经验模式分解, 能量矩