基于形态滤波与样本熵的转子故障特征提取方法

将样本熵引入旋转机械故障诊断领域,结合形态滤波和样本熵,提出了一种新的转子故障特征提取方法.首先选用最简单的直线结构元素,对实测转子振动信号进行形态滤波降噪处理;然后计算降噪后信号的样本熵,包括转子正常、不平衡、不对中、油膜涡动和碰摩等五种工况的振动信号;最后将样本熵作为特征,对各种故障状态进行评价.转子系统故障诊断的实例验证了该方法的可行性和有效性.

Feature Extraction of Rotor Fault Using Morphological Filtering and Sample Entropy

The sample entropy was introduced into the fault diagnosis field of rotating machinery.Combined with morphological filtering and sample entropy,a novel feature extraction method for rotor was proposed.Firstly,the line structure element was selected for morphological filter to denoise the original signal.Secondly,the sample entropy of denoised signal was calculated,the denoised signal types were normal,unbalanced,misalignment,oil-film whirl and rubbing.Finally,the sample entropy was used as fault feature to evaluate the different fault condition.Practical results prove that this method is effective on fault diagnosis of rotating machinery.