基于四轴联动的立铣刀故障诊断方法优化

2019-02-19

作者：田小静单位：西安航空职业技术学院

摘要：针对数控机床上的刀具磨损问题，为了有效检测刀具磨损状态，提出了基于EMD和SVM相结合的刀具故障检测方法。通过设备采集磨损的刀具信号并加以处理，然后利用经验模态分解（EMD）方法分解后再进行信号重组，得出若干个模态函数（IMF）；经标量量化处理得出特征向量后，利用支持向量机（SVM）方法完成刀具故障检测。结果表明，该方法能很好地检测出刀具磨损状态，验证了方法的可行性。

关键词：EMD；SVM；模态函数（IMF）；支持向量机

中图分类号：TG54；TH161文献标志码：ADOI：10.3969/j.issn.1000-7008.2019.02.033

Optimization of Tool Fault Diagnosis Method for Vertical Milling Tool

Based on Fouraxis End Milling Cutter

Tian Xiaojing

Abstract:In order to detect the state of tool wear effectively，a tool fault detection method based on EMD and SVM is proposed to solve the problem of tool failure NC machine tools.Through tool signal acquisition，by equipment and singnal noising，decomposition and synthesis，a number of modal functions(IMF) are obtained.And by scalar quantization processing to obtain the eigenvector，the tool fault detection is completed with support vector machine (SVM) method.The results show that the tool wear detection method is effective and the feasibility of the method is verified.

Keywords:EMD；SVM；modal function (IMF)；support vector machine；tool fault detection