基于主轴电流小波变换的刀具磨损状态监测

Tool Wear Condition Monitoring Based on Spindle Current Wavelet Transform

为消除机器人自动制孔系统中由于刀具磨损引起的孔径误差,提出一种基于主轴电流离散小波变换的刀具磨损状态在线监测以及寿命预测方法。首先根据主轴电流信号的波动规律,综合小波信号的正则性、相似性及曲线误差光滑性等要求,选用3阶多贝西小波基对主轴电流信号进行离散分解。结合刀具磨损规律,选取电流信号的3阶低频分解量作为刀具磨损状态监测的最有效特征,将电流信号的1阶高频分解量进行离散傅里叶变换,得到高频分量的频域特性,为电磁兼容设计提供依据;最后利用最小二乘法拟合刀具磨损量与主轴电流特征值的线性关系,通过监测主轴电流特征值,实现对刀具后刀面磨损量的在线监测以及刀具寿命预测。

In order to eliminate the hole diameter error caused by tool wear in robot automatic drilling system,a method based on spindle current discrete wavelet transform for tool wear state online monitoring and life prediction was proposed.Firstly,according to the fluctuation law of the spindle current signal and the requirements of the regularization,similarity and smoothness of the curve error of the wavelet signal,the 3rd-order Daubechies wavelet base is selected to carry out the discrete decomposition of the spindle current signal.Combined with the law of tool wear,the 3rd-order lowfrequency decomposition of the current signal is selected as the most effective feature of tool wear state monitoring.The first order high frequency decomposition of the current signal is carried out by discrete Fourier transform to obtain the frequency characteristics of the high frequency component,which provides a basis for electromagnetic compatibility design.Finally,the least square method was used to fit the linear relationship between tool wear and spindle current characteristic value,and realized the online monitoring of spindle tool wear state and the prediction of tool life by monitoring the characteristic value of spindle current.