改进集成噪声重构经验模式分解的微弱时频特征增强方法及应用*

基于噪声利用机制，集成噪声重构经验模式分解方法(Ensemble noise-reconstructed empirical mode decomposition， ENEMD)利用原信号中固有噪声分量改善模式混淆现象，并通过固有噪声分量的相互抵消作用实现信号降噪。然而，该方法中关键噪声估计技术采用类硬阈值处理方式，忽略系数之间相关性。为此，研究基于相邻系数降噪原理的ENEMD噪声估计技术，提高固有噪声分量估计的准确性。在此基础上，将改进ENEMD方法引入Hilbert-Huang变换中，提出改进ENEMD的微弱时频特征增强方法。该方法以无模式混淆的本征模式分量(Intrinsic mode function， IMF)准确表征微弱故障信号的瞬时频率，并以降噪IMF有效提高时频谱信噪比，消除时频谱中噪声杂点，显著提高信号时频表示的分辨率，增强微弱故障的时频表征并突显局部故障征兆，为机械早期和微弱故障识别提供有效手段。工程实例表明该方法有效揭示空气分离压缩机碰撞与摩擦故障征兆，并成功提取重油催化裂化机组早期微弱碰摩故障特征。

Weak Time-frequent Feature Enhancement Method Using Improved Ensemble Noise-reconstructed Empirical Mode Decomposition and Its Application

Based on the noise utilization mechanism, ensemble noise-reconstructed empirical mode decomposition (ENEMD) uses the noise component inherent in the input data to ameliorate the mode mixing problem and to cancel each other out by a collection in the mean IMFs given enough empirical mode decomposition (EMD) trials, yielding the signal denoising. However, the analogous hard thresholding is adopted in the pivotal noise estimation technique, ignoring the relativity among the coefficients. Thus, the noise estimation using the neighboring coefficient principle is investigated to improve the precision of noise estimation. On the basis, improved ENEMD is introduced to Hilbert-Huang transform (HHT) and weak time-frequent feature enhancement method using improved ENEMD is proposed. In the method, the instantaneous frequency by IMFs without the mode mixing could accurately characterize the weak fault signals. Meanwhile, by the denoised IMFs, the signal-to-noise ratio of HHT is effectively improved and the noise of HHT is restrained, which heavily enhance the resolution and weak faults of time-frequency features, highlighted the local fault symptoms. The proposed method provides an effective tool for mechanical early and weak fault identification. The engineering applications showed that the method could effectively reveal the impact and friction fault symptom from the compressor air separation, and successfully extract the early weak impact fault feature from the heavy oil catalytic cracking unit.