基于LMD和AR模型的转子系统故障诊断方法

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.

Fault Diagnosis Approach for Rotor Systems Based on LMD and AR Model

A fault diagnosis approach for rotor systems based on Local Mean Decomposition (LMD) and AR model was proposed. Firstly, by using LMD method, the vibration signal of rotor systems was decomposed into a number of product function (PF) components, whose instantaneous frequencies had physical meaning, and then the AR model of each PF component was established. Furthermore, the model parameters and the variance of remnant were regarded as the fault feature and served as the input parameter of neural networks to identify the condition and fault pattern of a rotor system. The study results have shown that both EMD and LMD method can be applied to the rotor system fault diagnosis effectively. However, the latter has better decomposition results.