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.
出处
《天津大学学报》
EI
CAS
CSCD
北大核心
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)
关键词
时变自回归
非平稳信号
谱估计
神经网络
time-varying autoregression（TVAR）
nonstationary signal
spectrum estimation
neural network
