基于粗糙集与偏相关分析的机床热误差温度测点约简

为了合理减少温度测点数量并有效提高温度数据采集与分析的效率，提出了一种基于粗糙集与偏相关分析相结合的温度测点约简方法。首先，利用偏相关分析的方法建立了温度变量与主轴热误差之间的偏相关系数，并以此为依据辨识了主要的敏感温度变量。然后，在基于粗糙集理论获取的可行温度测点组合基础上，筛选出包含敏感温度变量最多及偏相关度高的温度测点组合。最后，建立了热误差线性回归模型，并在某型号数控机床上进行验证与分析。结果表明：温度传感器测点可由22个减少到6个，在很大程度上提高了热误差模型的精确性和鲁棒性。

Reduction of Temperature Measurement Points for an NC Machine Tool Based on Rough Set and Partial Correlation Analysis

To reduce the number of temperature measurement points and improve the efficiency of temperature data acquisition, a new method of temperature measurement based on rough set and partial correlation analysis was proposed. First, based on the way of partial correlation analysis, the partial correlation coefficients between the temperature variables and thermal error of spindle were calculated, and it was used as the basis of choice of the main temperature sensitive variables. Then, the feasible temperature measuring points of the combination by rough sets were obtained, and the most sensitive temperature variables including temperature and partial correlation degree high point combination were screened. Finally, linear regression model of thermal error was established to test prediction accuracy, and verified in a certain type of CNC machine. Results show that temperature sensors are reduced from 22 to 6 to improve the precision and robustness of the thermal error model to a great extent.