基于EEMD与形态滤波的滚动轴承故障诊断方法研究

为从滚动轴承故障信号中提取出包含故障信息的特征频率,提出集合经验模式分解法(EEMD)与形态滤波相结合的解调方法。该方法首先利用EEMD自适应地将信号分解成多个IMF分量,然后计算各IMF分量与原信号的相关系数,选择合适的IMFs进行信号重构,再对重构后的信号进行形态滤波,滤除脉冲干扰,提取出故障特征信息。将该方法应用于滚动轴承故障诊断实例中,并将分析结果与直接对原信号进行包络谱分析解调的结果进行对比。结果表明,该方法提取故障信息的效果较包络谱分析解调的效果要好。

Fault diagnosis of rolling bearings based on EEMD and morphological filter

In order to extract characteristic frequency containing fault information from fault signal of rolling bearings, a demodulation method combining both correlation coefficient of ensemble empirical mode decomposition (EEMD) and morphological filter is proposed. This method uses EEMD to decompose the sample signal into several IMF components adaptively and then chooses appropriate IMFs to reconstruct signal after calculating the correlation coefficient of IMFs and the original signal. Following this, it carries out morphological filtering for reconstructed signal to filter out pulse interference and extracts the fault features. Application to fault diagnosis of rolling bearings shows that the proposed method can extract the fault features effectively and when compared with the results of envelope spectrum analysis, the proposed method proves to be more effective in fault feature extraction than the method of spectral envelope demodulation.