提出了基于局部均值分解(Local mean decomposition,简称LMD)和AR模型相结合的转子系统故障诊断方法.该方法先用LMD方法将转子振动信号分解成若干个瞬时频率具有物理意义的PF(Product function,简称PF)分量之和,然后对每一个PF分量建立AR模型,提取模型参数和残差方差作为故障特征向量,并以此作为神经网络分类器的输入来识别转子的工作状态和故障类型.与EMD方法的对比研究表明,这两种方法均能有效地应用于转子系统的故障诊断.但LMD方法信号分解后数据残差比EMD方法的小.
为了提取多分量调幅调频信号的幅值和频率信息,提出了基于局部均值分解(local mean decomposition,简称LMD)的能量算子解调机械故障诊断方法。该方法先利用LMD将机械调制信号分解成若干个乘积函数(production function,简称PF)分量,然后对每一个PF分量进行能量算子解调,获得信号的幅值和频率信息进行故障诊断。利用该方法对仿真信号以及轴承和齿轮故障振动信号进行实验研究的结果表明,基于LMD的能量算子解调方法能够有效地提取机械故障振动信号特征。
为了提取多分量调制信号的调制信息,研究了一种信号分析方法——局部均值分解(local mean decomposition,简称LMD)方法。LMD方法首先将一个多分量的调制信号自适应地分解成若干个具有一定物理意义的PF(product function)分量,其中每个PF分量为一个包络信号和一个纯调频信号的乘积,然后求出每个PF分量的瞬时幅值与瞬时频率,从而获得原信号完整的调制信息。本文用LMD方法对仿真信号以及齿轮故障振动信号进行了分析,结果表明该方法能有效地提取出信号的调制信息。
Aiming at the non-stationary feattwes of the roller bearing fault vibration signal, a roller bearing fault diagnosis methtxt based on improved Local Mean Decomposition （LMD） and Support Vector Machine （SVM） is proposed. In this paper, firstly, the wavelet analysis is introduced to the signal decomposition and reconstruction; secondly, the LMD method is used to decompose the recomtnion signal obtained by the wavelet analysis into a ntmaber of Product Ftmctions （PFs） that include main fault characteristics, thus, the initial feattwe vector matrixes could be formed automatically; Thirdly, by applying the Singular Valueition （SVD） techniques to the initial feature vector matrixes, the singular values of the matrixes can be obtained, which can be used as the fault feature vectors of the roller bearing and serve as the input vectors of the SVM classifier; Finally, the recognition results can be obtained from the SVM output. The results of analysis show that the propsed method can be applied to roller beating fault diagnosis effectively.
