基于LMD近似熵和支持向量机的轴承故障诊断

Diagnosing Faults of Bearings with LMD Approximate Entropy and SVM

针对轴承振动信号的非平稳特征和现实中难以获得大量典型故障样本的情况,提出了一种基于局部均值分解(local mean decomposition,LMD)的近似熵和支持向量机的轴承故障诊断方法。首先通过LMD方法将非平稳的原始加速度振动信号分解成若干个平稳的乘积函数(productionfunction,PF);轴承发生不同的故障时,在不同频带内的信号近似熵值会发生改变,故可通过计算不同振动信号的LMD近似熵判断是否发生故障和发生的故障类型;从包含有主要故障信息的PF分量中提取出来的近似熵特征作为输入建立支持向量机(support vector machine,SVM),判断轴承的工作状态和故障类型。

The vibration signals of bearings are usually typical fault samples in reality. Therefore we propose a tion （LMD） approximate entropy and the support vector non-stationary and it is difficult to obtain a large number of fault diagnosis method that uses the local mean decomposi- machine （SVM）. We decompose the original non-stationa- ry acceleration vibration signals into several stationary production functions （PFs）. Because the approximate entro- py values of vibration signals in different frequency bands change when faults occur in bearings, we can judge whether a fault occurs and decide its type by calculating the approximate entropy and can establish the SVM by using as its input the approximate entropy features extracted from the PFs that contain the major information on faults. Experimental results show that our diagnosis method can effectively diagnose faults of bearings.