摘要
针对现有的时变自回归(Time-Varying Autoregressive,TVAR)模型应用于滚动轴承故障诊断中的问题,提出一种前向估计与后向估计相结合的改进模型。该模型在引入时变遗忘因子的基础上,定义了前后向联合估计的均方误差并对基函数的加权系数求偏导,得到加权系数的计算公式,然后利用递推最小二乘(Recursive Least Squares,RLS)方法推导了该计算公式的递推形式。针对滚动轴承内圈故障的仿真和实验信号,使用改进前后的模型进行时频分析。仿真和实验结果表明,改进后的模型有效地克服了现有模型无法获得初始时刻频率估计的缺点,具有更高的时频估计精度、更强的抗噪声能力,能够更加有效地提取滚动轴承的故障特征频率。
An improved time-varying autoregressive model was established for roiling bearing fault diagnosis based on combination of forward and backward estimation. By adopting time-varying forgetting factor, mean squared error based on forward and backward estimation was defined and partial derivatives were derived for weighted coefficients of basis functions to obtain their calculation formulas. Then, the recursion formulas of the weighted coefficients were derived using recursive least squares （RLS）. Time-frequency analysis for simulation and experimental signals of a faulty bearing inner ring was conducted using the improved and unimproved models. The results showed that the improved model can overcome the unavailability of frequency estimation at the initial time, it has higher accuracy in temporal and frequency estimation, and better anti-noise performance; so the improved model can extract fault feature frequency of rolling bearing more effectively.
出处
《振动与冲击》
EI
CSCD
北大核心
2011年第12期74-77,107,共5页
Journal of Vibration and Shock
基金
国家自然科学基金(51075330
50975231)
关键词
改进时变自回归模型
递推最小二乘法
滚动轴承
故障诊断
improved time-varying autoregressive （TVAR） model
recursive least squares （RLS）
rolling bearing
fault diagnosis
