基于CEEMDAN和优化形态学滤波的电机轴承故障诊断

Fault Diagnosis of Motor Bearings Based on CEEMDAN and Optimized Morphological Filtering

摘要: 电机轴承在运行过程中，存在较多的不规则噪声干扰分量，使得对电机轴承故障诊断存在一定的难度，针对上述背景，本文提出了一种基于完全集合经验模态分解和优化形态学差值滤波的新方法，用于电机轴承的故障诊断。通过使用西储大学故障轴承振动数据对算法的有效性进行验证。完全集合经验模态分解能够有效分解非线性、非平稳信号，提供精细的分解信号。随后筛选出包含故障特征的分量信号进行重构，利用优化后形态学差值滤波器对重构信号进行去噪处理，以增强故障信号特征。实验结果表明，该方法在提取故障特征方面具有高精度和鲁棒性，显著优于传统方法。

Abstract: During the operation of motor bearings, there are more irregular noise interference components, which makes it difficult to diagnose the faults of motor bearings. In view of the above background, this paper proposes a new method based on the complete EEMD with adaptive noise and optimal morphological difference filtering for the fault diagnosis of motor bearings. The effectiveness of the algorithm is verified by using vibration data of faulty bearings from Western Reserve University. The complete ensemble empirical modal decomposition can effectively decompose nonlinear and nonsmooth signals and provide fine decomposition signals. Subsequently, the component signals containing fault features are screened for reconstruction, and the reconstructed signals are denoised using an optimised morphological difference filter to enhance the fault signal features. The experimental results show that the method has high accuracy and robustness in extracting fault features, which is significantly better than the traditional method.