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存在于机床工作台和导轨之间的滑动摩擦直接影响刀具的速度和位置,进而影响加工工件的表面质量。对机床传动链分解,分别提取电机负荷电流,对电流信号做5层小波包分解,利用小波包熵值提取能反映摩擦信息的特征量D值。对机床导轨加润滑油之后采集四个半小时之内55组电流信号数据,提取电流信号D值做趋势分析,通过D值随时间的变化曲线可以观察出润滑状态经历过的三个时期。实验表明,利用小波包熵值在电流信号中提取的特征量能反映润滑状态的变化情况。
The sliding friction existing between the machine table and the guide rail directly affects the speed and position of the tool, thus affecting the surface quality of the machined workpiece. Decomposing the transmission chain of the machine, respectively extracting the load current of the motor, decomposing the current signal into five layers of wavelet packets, and extracting the characteristic value D of the friction information by using the wavelet packet entropy. 55 sets of current signal data are collected within four and a half hours after the lubricating oil is added to the guide rail of the machine tool. The D value of the current signal is extracted to do the trend analysis. Three periods of the lubricating state are observed through the curve of the D value with time. Experiments show that using wavelet packet entropy in the current signal extracted features can reflect the changes of lubrication conditions.