基于LMD和AR模型的转子系统故障诊断方法

龙源期刊网 基于LMD和AR模型的转子系统故障诊断方法
作者：杨宇,杨丽湘,程军圣
来源：《湖南大学学报·自然科学版》2010年第09期
摘要: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方法在信号分解方面体现了更大的优势。

关键词:LMD;AR模型;转子系统;故障诊断
Fault Diagnosis Approach for Rotor Systems Based on
LMD and AR Model
YANGYu, YANG Lixiang, Cheng Junsheng
(State key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Changsha, 410082)
Abstract: A fault diagnosis approach for rotor systems based on Local mean decomposition (LMD) and AR model is proposed. Firstly, by using LMD method, the vibration signal of rotor systems is decomposed into a number of product function (PF) components whose instantaneous frequencies own physical meaning, and then the AR model of each PF component is established. Furthermore, the model parameters and the variance of remnant are regarded as the fault feature and served as input parameter of neural networks to identify the condition and fault pattern of a rotor system. The study results show that both EMD and LMD method could be applied to the rotor systems fault diagnosis effectively. However, the latter has better decomposition results.
Key word: Local mean decomposition; AR model; Rotor systems; Fault diagnosis
当转子系统发生故障时,由于系统载荷、刚度等多方面的影响,其振动信号多表现为复杂的非平稳信号[1]。

如何从非平稳信号中提取特征向量是转子系统故障诊断的关键,传统的故障诊。