非平稳信号的时变自回归建模及其在轴承故障诊断中的应用  

Modeling of Nonstationary Signals Based on Time-Varying Autoregression and Its Application in Fault Diagnosis of Bearing

作　　者：王国锋[1] 罗志高[1] 秦旭达[1] 冷永刚[1] 常乐[1] 

机构地区：[1]天津大学机械工程学院,天津300072

出　　处：《天津大学学报》2008年第5期558-562,共5页Journal of Tianjin University(Science and Technology)

基　　金：国家自然科学基金资助项目(50675153);北京市先进制造技术重点实验室开放项目(10200531)

摘　　要：基于时变自回归(TVAR)方法实现了非平稳随机信号的参数化建模,提出采用最小信息准则确定模型阶数.通过多分量线性调频仿真信号的时变谱估计,表明该方法分辨率高,没有交叉项的干扰,计算速度快.在仿真分析的基础上,应用参数化时频谱和BP神经网络方法进行滚动轴承故障信号的分类和辨识,并基于能量法对时频图进行特征提取.分析结果表明,时变自回归方法的拟合精度高,能有效提取轴承故障信号特征,同时结合神经网络能对故障进行准确诊断.Parametric-modeling of nonstationary signal based on time-varying autoregression （TVAR） was realized. Akaike information criterion, which can choose the order automatically, was expatiated. Time-varying spectrum estimation of multi-ponderance linear frequency modulation signal proves that the TVAR has lots of merits, such as high resolution, without cross term and fast computing speed. The parametric time-varying spectrum and BP neural network method were used to classify and distinguish fault signal of beating.The time-varying spectrum features were extracted by energy means. Results show that the TVAR can extract the characteristic of fault signal, gain high simulation precision and identify fault types exactly.

关 键 词：时变自回归 非平稳信号 谱估计 神经网络 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]