Application Research on Recursive Feature Elimination and Extra Trees in Milling Cutter Wear Monitoring
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摘要: 针对金属铣削过程中刀具磨损监测问题，本文提出了一种基于递归特征消除和极端随机树相结合的刀具磨损监测模型。首先对力、振动和声发射信号的时域、频域特征进行提取，分别采用逻辑回归、分类与回归树、线性回归、线性判别分析作为递归特征消除的基模型进行特征降维。再利用处理后的特征对K近邻、支持向量回归、极端随机树模型进行训练，得出多种监测模型。通过对比刀具磨损拟合曲线图和分析评估结果的标准差，可得出基模型为分类与回归树的递归特征消除，与极端随机树算法相结合模型拟合度达到99.74%，评估结果的标准差为4.04。结果表明该方法能够实现对铣刀磨损的有效监测，从而提高零件加工质量。Abstract: To solve the problem of tool wear monitoring in metal milling, a tool wear monitoring model is proposed based on recursive feature elimination and extra trees algorithm. Firstly, the time and frequency domain features of force, vibration and acoustic emission signals are extracted. Logistic regression, classification and regression tree, linear regression and linear discriminant analysis are used as base model of recursive feature elimination for feature dimension reduction. Then the processed features are used to train the K nearest neighbor, support vector regression and extra trees models, and a variety of monitoring models are obtained. By comparing the tool wear fitting curve and analyzing the standard deviation of the evaluation results, it can be concluded that the base model is the recursive feature elimination of classification and regression tree. The fitting degree of the model combined with the extreme random tree algorithm reaches 99.74 %, and the standard deviation of the evaluation result is 4.04. The results show that this method can effectively monitor the wear of milling cutter, so as to improve the machining quality of parts.
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表 2 特征排序
名次 LR1-RFE CART-RFE LR2-RFE LDA-RFE 1 频率平均值Vy 频率的根方差Vx 最小频率Vy 频率的峰度Fy 2 频率平均值AE Peak value Fy 最小频率Fz 频率的峰度Fz 3 频率平均值Fy 频率平均值Vx 最小频率Vx 频率的峰度Fx 4 频率平均值Vx 频谱均值Vx 最小频率Fy 频率的峰度AE 5 频率平均值Fx THD Fx 最小频率AE 频率的峰度Vy $\vdots $ $\vdots $ $\vdots $ $\vdots $ $\vdots $ 156 频率的峰度Fx 频率的峰度Vz 频率的偏斜系数AE 频率平均值Vy 157 频率的峰度Fz 频率的峰度AE 频率平均值Fz 频率平均值Fz 
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表 3 决策树数量评分
决策树数量 评分结果 标准方差 20 −99.417794 14.471892 30 −103.125864 15.818076 40 −98.549355 14.460887 50 −97.995295 13.652701 60 −96.014613 14.592283 70 −100.100593 14.644818 80 −96.183946 15.307290 90 −98.718908 16.066450
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表 4 模型拟合度（评估结果的标准差）
特征集 特征个数 KNN SVC ET LR1-RFE （FD1） 5 95.38%
（72.43）91.93%
（126.48）96.67%
（52.19）CART-RFE （FD2） 9 99.33%
（10.47）96.17%
（59.95）99.74%
（4.04）LR2-RFE （FD3） 153 99.02%
（15.31）98.72%
（19.93）99.82%
（2.76）LDA-RFE （FD4） 157 98.95%
（16.41）98.77%
（19.28）98.51%
（23.28）
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