作　　者： ; ;

机构地区： 郑州大学西亚斯国际学院

出　　处： 《机械设计与制造》 2015年第2期210-213,共4页

摘　　要： 针对滚动轴承非平稳性的振动信号,提出了基于局部均值分解(Local Mean Decomposition,LMD)及马氏距离敏感阈值的滚动轴承故障诊断方法。首先,对振动信号进行LMD分解,获得一系列乘积函数(Production Function,PF),有的PF分量包含的故障信息多,有的包含的少,为此采用K-L散度法提取出主要PF分量;计算主要PF分量的时域参数指标,将其组合成特征向量,根据马氏距离提出马氏距离敏感阈值来表征不同的故障状态,取多组正常信号的特征向量均值作为标准特征向量,计算未知特征向量与标准特征向量的马氏距离敏感阈值,从而对其故障状态进行识别。试验结果表明,在不同转速下,该方法能够有效的对滚动轴承故障进行识别,且效果较EMD方法好。 Aiming at the no stationary characteristic of a gear fault vibration signal, a method based on local mean decomposition and Mahalonobis distance sensitive threshold is proposed. First, the vibration signal is decomposed to be a series PF component by LMD, some of PF component contains more data on the fault than other, and a method based on kullback-leibler divergence is used to extract the principal PF component; then calculating domain parameter index of principal PF components, which are combined into a feature vector, the Mahalanobis distance sensitive thresholds based on Mahalanobis distance is proposed to correspond to different fault conditions, and multiple sets of normal signal feature vectors are used as a standard feature vector mean, and calculate the Mahalanobis distance sensitive threshold between standard feature vector and unknown feature vector, identify their fanlt condition. The results showe that this method can effectively identify the fault of rolling bearing in different speed, and it is better than the EMD method.

关 键 词： 滚动轴承 散度 马氏距离 故障诊断

领　　域： [机械工程]