局部Hilbert边际能量谱在滚动轴承故障诊断中的应用

A Study of Rolling Bearing Fault Diagnosis Based on Local Hilbert Marginal Energy spectrum

针对滚动轴承振动信号非线性的特点,提出一种基于局部希尔伯特边际能量谱和马氏距离判别法相结合的故障诊断方法。首先,采用镜像延拓方法抑制经验模态方法将待分析信号分解成多阶固有模态函数和的形式,并根据相关性判别算法选取含有主要故障信息的IMF分量;其次,利用局部Hilbert边际能量谱提取故障信号能量特征参数;最后,利用马氏距离算法对滚动轴承的工作状态进行判别。通过相关试验证明了此方法的有效性,具有一定的工程应用价值。

Aiming at the non-linear feature of the rolling bearing vibration signal,a rolling bearing fault diagnosis method based on local Hilbert marginal energy spectrum and Mahalanobis distance is proposed.Firstly,the vibration signal is decomposed by improved empirical mode decomposition,and a set of the intrinsic mode function is obtained and the mode functions which containing the main fault information are selected by the false evaluation method based on correlation analysis technique.Then,energy characteristic parameters of fault signal are extracted from each IMF component by using local Hilbert marginal energy spectrum.Finally,Mahalanobis distance is used to identify the rolling bearing fault pattern.The experiment results show that this method can identify rolling bearing fault patterns effectively and offer a practical method for its fault diagnosis.