基于瞬时频率估计与Vold-Kalman滤波的铣削颤振识别

Chatter detection in milling process based on instantaneous frequency estimation and the Vold-Kalman filter

颤振严重制约了高速铣削加工效率。动态铣削力信号具有非线性、非平稳的特点,常规的信号分解方法难以处理该类信号。提出了一种基于瞬时频率估计和Vold-Kalman滤波的多分量信号分解方法,并运用该信号分解方法识别颤振。基于频谱集中性指标估计信号的瞬时频率参数;用Vold-Kalman滤波器提取对应参数的各信号分量;由于颤振时铣削力信号的能量分布在频域发生变化,由此引入能量熵的定义。采用分解得到的子信号能量熵变化来识别颤振。实验分析表明该方法有效可行。

Chatter is the major factor affecting the efficiency of high-speed milling.Chatter signal in milling has obvious nonlinear and non-stationary properties.It is difficult for the conventional signal analysis method to deal with signals in such category.This paper presented a multi-component signal decomposition method based on instantaneous frequency estimation and the Vold-Kalman filter.And the signal decomposition method was applied to chatter detection.First,the instantaneous frequency parameters of the signals were estimated based on the spectral concentration index.Then,the Vold-Kalman filter was applied to extract the signal components corresponding to the estimated parameters.Since the energy distribution of the milling force signal changed in the frequency domain as a result of chatter,the definition of energy entropy was introduced.Finally,chatter was identified by the change of energy entropy of the sub-signal.The experimental results show that the method is effective and feasible.