基于RSGWPT和EEMD的滚动轴承故障诊断

Rolling element bearing fault diagnosis based on RSGWPT and EEMD method

针对较强噪声环境下的滚动轴承故障识别问题,提出并研究了一种新的滚动轴承故障诊断技术,采用将冗余二代小波包变换(RSGWPT)和集合经验模态分解(EEMD)相结合提取故障特征的方法。仿真实验和振动信号诊断结果表明,此方法可以提取特征频率,有效抑制噪声,根据实际数据准确地诊断出滚动轴承的故障类型,为强噪声背景下提取弱信号开辟了新思路。

Aiming at the problem of rolling element bearing fault identification in the strong noise environment, a novel method of fault diagnosis for rolling element bearing is proposed and studied. This method implements an analysis combining redundant second generation wavelet packet transform（RSGWPT） and ensemble empirical mode decomposition（EEMD） to extract the fault characteristics from the measured signal. The simulation experiments and vibration signal diagnosis results show that the proposed method can extract the characteristic frequency and suppress the noise effectively, and can diagnose the fault type of rolling bearing accurately according to the actual data.