基于鲁棒局部均值分解与二阶瞬态提取变换的滚动轴承故障诊断

利用传统故障诊断方法对滚动轴承进行诊断时存在故障特征提取困难以及提取特征不明显的问题。针对此问题，提出了一种基于鲁棒局部均值分解（robust local mean decomposition，RLMD）以及二阶瞬态提取变换（Second-order transient-extracting transform，STET）的故障特征提取方法。首先对滚动轴承故障信号进行RLMD处理，得到一系列故障信息丰富的特征分量。然后利用二阶瞬态提取变换善于提取信号中强脉冲分量的特点，对筛选出的分量进行二阶瞬态提取变换以提取脉冲故障特征进行诊断分析。实验分析结果表明，该方法能够有效地提取出故障特征，且特征提取效果优于传统诊断方法，适用于滚动轴承故障诊断。

Fault Diagnosis of Rolling Bearing Based on RLMD and STET

Using traditional fault diagnosis methods to diagnose rolling bearings existing problems like fault features extracting difficult and fault features hard to distinguish. To solve these problem, a fault feature extraction method based on robust local mean decomposition (RLMD) and the second-order transient-extracting transform( STET) is proposed in this paper. Firstly, the fault vibration signals of rolling bearing is processed by RLMD, and several components with rich fault feature information is obtained. Then take advantage of the characteristics that the strong pulse component of the signal can getting by second-order transient-extracting transform. The second order transient extraction transformation is carried out to extract the pulse fault features of the selected component for diagnosis and analysis. Experimental results show that the proposed method can effectively extracting fault features, The proposed method has better ability of feature extraction than the traditional diagnosis methods and is suitable for the fault diagnosis of rolling bearing.