基于LMD能量特征的滚动轴承故障诊断方法

针对滚动轴承故障振动信号的多载波多调制特性,提出一种基于局域均值分解(local mean decomposition,简称LMD)能量特征的特征向量提取方法,并与支持向量机相结合用于滚动轴承的故障诊断。首先,采用LMD方法将复杂调制振动信号分解为若干单分量信号乘积函数(production function,简称PF);然后,对反映信号主要特征的PF基于时间轴积分,得到各PF分量能量矩并构造特征向量;最后,将其输入多分类支持向量机中,用于区分滚动轴承的故障类型与故障程度。对滚动轴承内圈故障、外圈故障及滚动体故障振动信号的分析结果表明,该方法能有效提取滚动轴承各工作状态信号的故障特征,能准确识别故障类型,同时对故障程度的判断表现出较高的识别率。

Fault Diagnosis of Rolling Bearing Based on Energy Feature of Local Mean Decomposition

The vibration signals of a rolling bearing usually exhibit characteristics of multi-carrier and multi-modulation. In light of this problem, an eigenvector extracting method based on the local mean decomposition (LMD) energy feature is proposed. The method is combined with the support vector machine and applied to fault diagnosis of rolling bearings. The complicated modulation signal is decomposed into a set of product functions (PF) using LMD. Then, the energy moment of PFs containing the fault characteristics is obtained by calculating the integral of PF as input parameters of a support vector machine classifier to sort the fault patterns and degrees of rolling bearings. To analyze the vibration signals acquired from the bearings with inner-race, outer-race or element faults, experimental results indicate that the proposed method can effectively extract the fault characteristics, accurately identify the type of rolling bearings and achieve a comparatively high correction-identification ratio of the fault level.