改进 CEEMDAN 算法的电机轴承振动信号降噪分析

为提高传统自适应噪声完备经验模态分解算法(CEEMDAN)对电机轴承故障特征信号的精确提取率,降低重构信号失 真,提出了一种改进自适应噪声完备经验模态分解算法。 首先利用传统 CEEMDAN 对原始信号初步分解,获得若干特征分量 (IMFs)和固有模态分量,将若干 IMFs 运用熵权法进行初步故障特征信号消噪和提取,对筛选后的 IMF 分量进行二次分解和二 次筛选,获得典型故障敏感信号,再运用 SG(Savitzky-Golay)平滑滤波进行信号重构,最终实现电机轴承信号降噪。 最后利用凯 斯西储大学轴承数据进行改进算法性能分析,结果表明该方法对电机轴承信号能够有效的进行信号降噪,其信噪比相比于原始 信号提高 2. 2 dB。

Noise reduction analysis of motor bearing vibration signal based on improved CEEMDAN algorithm

In order to improve the accurate extraction rate of the traditional complete ensemble empirical mode decomposition with Adaptive Noise ( CEEMDAN) for motor bearing fault characteristic signals and reduce the distortion of the reconstructed signal, an improved CEEMDAN algorithm is proposed. The original signal is initially decomposed using traditional CEEMDAN to obtain several feature components (IMFs) and intrinsic modal components. Some IMFs are de-noised and extracted by entropy weight method. The filtered IMF components are secondary decomposed and secondary screened to obtain typical fault sensitive signals. Then the signal reconstruction is carried out by using SG ( Savitzky-Golay) smoothing filter and the motor bearing signal is de-noised. Finally, the performance of the improved algorithm is analyzed by using the data of Case Western Reserve University. The results show that the method can effectively reduce the signal noise of the motor bearing signal, and its SNR is improved by 2. 2 dB compared with the original signal.