基于自步学习的刀具加工过程监测数据异常检测方法
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张建, 胡小锋, 张亚辉
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Abnormal Detection Method of Tool Machining Monitoring Data Based on Self-Paced Learning
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ZHANG Jian, HU Xiaofeng, ZHANG Yahui
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表4 不同异常检测算法对比结果
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Tab.4 Comparison results of different anomaly detection algorithms
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方法 | MAE | RMSE | MAE’/ % | RMSE’/ % | 未筛选 | 1.45 | 1.816 | — | — | 本文方法 | 1.069±0.067 | 1.304±0.075 | 26.28 | 28.19 | LOF | 1.219 | 1.486 | 15.93 | 18.17 | DBSCAN | 1.693 | 2.137 | -16.76 | -17.68 | 孤立森林 | 1.263 | 1.527 | 12.90 | 15.91 | K均值 | 1.844 | 2.215 | -27.17 | -21.97 | One-Class SVM | 1.28 | 1.605 | 11.72 | 11.62 | 随机采样 | 1.485±0.327 | 1.877±0.456 | -2.41 | -3.37 |
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