基于CEEMDAN能量熵和马氏距离的齿轮箱轴承故障诊断方法

Gearbox Bearing Fault Diagnosis Method Based on CEEMDAN Energy Entropy and Mahalanobis Distance

针对齿轮箱轴承信号非平稳性及其故障特征难以提取的问题,提出一种自适应白噪声平均总体经验模态分解(CEEMDAN)能量熵和马氏距离相结合的故障诊断方法。首先采用CEEMDAN方法对非平稳的轴承故障信号进行分解,获得若干阶表征信号特性的固有模态函数(IMF)分量;然后计算各IMF分量的自相关函数和相关系数,以滤除信号内的噪声干扰和对故障特征不敏感的IMF分量;最后计算各敏感故障特征分量的能量熵,将其作为特征参数形成状态特征向量,并使用马氏距离判别方法对轴承的工作状态和故障类型进行诊断。通过对实测不同工况以及不同故障程度的齿轮箱轴承信号的分析,证明了所提方法的有效性。

Aiming at the vibration signals fault feature of gearbox bearing was hard to extract,a method based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and Mahalanobis distance was proposed.The vibration signal was decomposed by CEEMDAN to get several intrinsic mode functions(IMFs).Then,the noise and false mode functions were filtering by autocorrelation function and correlation coefficient,the components that characterized the signal were obtained.Finally,the energy entropy of each sensitive fault component was calculated,forming a state feature vector as a feature parameter;the working condition and fault type of the bearing were diagnosed by using the Mahalanobis distance method.The effectiveness of the proposed method is proved by analyzing the gearbox bearing signals measured under different working conditions and different fault degrees.