基于LMD的包络谱特征值在滚动轴承 故障诊断中的应用

滚动轴承故障振动信号往往是多分量的调幅-调频信号,而传统包络分析方法需要根据经验设置滤波器的中心频率与带宽,因而会带来解调误差.基于此,提出了一种基于局域均值分解(local mean decomposition,简称LMD)的包络谱特征值的滚动轴承故障诊断方法.该方法可以将一个多分量的调幅-调频信号分解成若干瞬时频率具有物理意义的PF (product function,简称PF )分量之和,由于每一个PF分量是分量包络信号和纯调频信号的积,因此可以直接对包络信号进行频谱分析得到包络谱.然后定义信号在包络谱中不同故障特征频率处的幅值比为包络谱特征值,并以此作为特征向量输入到支持向量机分类器中,用以区分滚动轴承的工作状态和故障类型.对滚动轴承正常状态、内圈故障和外圈故障振动信号的分析结果表明了该方法的有效性. 

Application of envelope spectrum characteristics based on LMD to roller bearing fault diagnosis

Most roller bearing fault vibration signals are always multi-component amplitude-modulated and frequency-modulated (AM-FM) signals,however,the selection of central frequency of band-pass filter needs to be determined with experience in conventional envelope analysis method,which would bring demodulation error.To target this problem,a roller bearing fault diagnosis approach based on LMD (local mean decomposition) and envelope spectrum characteristics was proposed.Firstly,by using LMD method,the vibration signal of roller bearing can be decomposed into a set of product functions (PF) whose instantaneous frequencies owned physical sense and each of which was a product of an envelope signal and a purely frequency modulated signal.Secondly,spectrum analysis can be used directly to envelope signal and then the envelope spectrum can be obtained.Finally,the amplitudes ratios in different characteristic frequencies of envelope spectrum were defined as the envelope spectrum characteristics,which served as input parameters of support sector machine classifier to classify working condition and fault patterns of roller bearings.The analysis results demonstrate the effectiveness of the proposed method.