基于SVD-AR模型与VPMCD的轴承故障诊断方法

摘要

针对强噪声背景下振动信号故障特征难以提取的问题,提出了基于奇异值分解的自回归(SVD-AR)模型,用于提取振动信号的特征,并与变量预测模型模式识别(VPMCD)方法相结合应用于轴承故障诊断.对轴承振动信号进行SVD;然后,利用奇异值差分谱对分量信号进行筛选,对能够反映故障信息的分量信号建立AR模型,提取轴承振动信号的特征信息;采用VPMCD对滚动轴承运行状态进行识别.实验证明了方法的合理性和有效性.%Aiming at problem of the failure feature extraction of vibration signal under strong noise background,proposed based on the singular value decomposition and auto regressive (SVD-AR) model,used for feature extraction of the vibration signal and applied for bearing fault diagnosis by combining with the variable predictive model based class discriminate (VPMCD).Firstly,the bearing vibration signal is decomposed by SVD,and then the component signals are screened by using the singular value difference spectrum,and AR model is established for the component signal which can reflect the fault information,extracting characteristic information of bearing vibration signal.Finally,it's used to identify the running state of the rolling bearing by VPMCD.The test of rolling bearing fault diagnosis proves that the method is reasonable and effective.