EMD和平滑伪Wigner-Ville谱熵的轴承故障诊断

摘要

提出一种基于经验模态分解(EMD)和平滑伪Wigner-Ville分布(SPWVD)谱熵的滚动轴承故障诊断的方法。EMD方法充分保留信号本身的非线性和非平稳特征，在信号的滤波和去噪中具有较大的优势，SPWVD谱熵用于定量刻画轴承不同状态下振动信号的时频能量分布，将二种算法相结合应用于不同工作状态滚动轴承，并设计最小二乘支持向量机(LS-SVM)智能模型，实现轴承状态和故障类型的自动分类和识别。通过SPWVD谱熵与谱峭度法的对比，验证了SPWVD谱熵的有效性。实验表明此方法能够有效地提取轴承故障的特征信息，提高轴承故障诊断率。%A method of fault diagnosis for rolling bearings based on empirical mode decomposition (EMD) and smoothed pseudo Wigner-Ville distribution (SPWVD) spectral entropy is proposed. In this method, the nonlinear and non-stationary characteristics of the signal in the EMD method, which has a great advantage in signal filtering and de-noising, are fully reserved. The SPWVD spectral entropy is used to quantitatively characterize the time-frequency energy distribution of the vibration signals in different states of the bearing. The intelligent model is designed based on the least square support vector machines (LS-SVM). The automatic classification of bearing state and identification of fault type of the bearing are realized. Through the mutual comparison of the SPWVD spectral entropy method and spectral kurtosis method, the effectiveness of the SPWVD spectral entropy is verified. The results show that this method can effectively extract the characteristics of the bearing fault information and improve the rate of bearing fault diagnosis.