基于CART决策树的电磁信号齿轮磨削烧伤电磁信号融合评价

Fusion Evaluation of Electromagnetic Signals for Gear Grinding Burns Based on CART Decision Tree

针对涡流检测、漏磁检测和巴克豪森检测三种无损检测方法中单一检测方法存在局限性和不稳定性的问题,提出了基于监督学习的CART决策树数据融合算法,采用该算法建立模型对典型工件是否存在磨削烧伤进行了评估。对某直齿工件的946个齿面检测信号样本数据进行试验,选用758组数据作为训练样本,建立剪枝后模型,对剩余188组数据做出评估,结果表明预测准确率为99.5%。说明基于监督学习的CART决策树算法识别精度高,为齿轮磨削烧伤的电磁无损评估提供了新思路。

Aiming at the limitation and instability of the single detection method among the three non-destructive detection methods of eddy current detection,magnetic flux leakage detection and Barkhausen detection,a CART decision tree data fusion algorithm based on supervised learning is proposed to grind typical work pieces.Burns are evaluated.Experiments are performed on the sample data of 946 tooth surface detection signals of a straight tooth work piece.758 sets of data are used as training samples to establish a model after pruning.The remaining 188 sets of data are evaluated.The results show that the prediction accuracy is99.5%.It shows that the CART decision tree algorithm based on supervised learning has high recognition accuracy and provides new ideas for the electromagnetic non-destructive evaluation of gear grinding burns.