Research of health monitoring technology for CNC machine tools based on ICEEMDAN-RCMWPE and WOA-KELM
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摘要: 针对数控机床刀具健康状态特征提取不足以及识别准确率低等问题，提出了一种刀具健康状态监测方法。首先，利用改进完全集合经验模态分解（ICEEMDAN）和精细复合多尺度加权排列熵（RCMWPE）进行信号处理与特征提取，提取出能够表征刀具健康状态的特征信息；其次经t-SNE处理，实现特征信息的降维与融合；最后将获取的低维特征输入到构建的鲸鱼优化核极限学习机（WOA-KELM）健康状态识别模型，从而对刀具健康状态进行分类与识别。经实验验证表明，所提出的信号处理、特征提取以及状态识别模型在刀具健康状态监测方面取得了很好的成效，其状态识别准确率高达99.76%，能够高效、准确地对刀具磨损状态进行分类和识别。Abstract: Aiming at the problems of insufficient extraction of tool health status features and low recognition accuracy of CNC machine tools, this paper proposes a tool health status monitoring method. Firstly, the improved complete set empirical mode decomposition (ICEEMDAN) and the fine composite multi-scale weighted permutation entropy (RCMWPE) are used for signal processing and feature extraction, and the feature information that can characterize the health state of the tool is extracted. Secondly, by t-SNE processing, the dimensionality reduction and fusion of feature information are realized. Finally, the obtained low-dimensional features are input into the constructed whale-optimized kernel extreme learning machine (WOA-KELM) health recognition model, so as to classify and identify the tool health state. Experimental verification shows that the proposed signal processing, feature extraction and state recognition model have achieved good results in tool health monitoring, and its state recognition accuracy is as high as 99.76%, which can efficiently and accurately classify and identify tool wear status.
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Key words:
- CNC machine tools /
- wear status monitoring /
- ICEEMDAN /
- RCMWPE /
- Kernel extreme learning machine
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表 1 铣削实验主要设备和切削条件
硬件条件 型号和主参数 切削条件 参数 数控铣床 高速数控机床
Roder Tech RFM760主轴转速/
（r/min）10 400 测力仪 Kister 9265B三向测力仪 进给速度/
（mm/min）1555 电荷放大器 Kister5019A多通道电荷
放大器轴向切削深度/mm 0.2 切削材料 Inconel718，长方形 径向切削宽度/mm 0.125 刀具 球头硬质合金具，
3切削刃每次走刀进
给量/mm0.001 数据采集卡 NIDAQ数据采集卡 采样频率
/kHz50 磨损测量器 LEICA MZ12显微镜 冷却条件 干切 
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表 2 刀具状态划分结果
类别 走刀次数/次 切削刃磨损量平均值/μm 初期磨损 1～50 39.6～87.3 正常磨损 51～225 88.1～124.7 急剧磨损 226～300 125.1～156.1 磨钝失效 300～315 156.6～165.2
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表 3 各方法状态识别准确率
模型 错分类样本数 准确率/（%） EEMD-RCMWPE-WOA-KELM 5 95.61 CEEMDAN-MWPE-WOA-KELM 3 97.42 ICEEMDAN-CMWPE-WOA-KELM 2 97.92 CEEMDAN-RCMWPE-WOA-KELM 2 98.43 ICEEMDAN-RCMWPE-WOA-KELM 1 99.76
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