摘要
总体经验模态分解(EEMD)方法在EMD的基础上消除了模态混叠的现象,从而更能准确地揭露出信号特征信息。根据声发射信号的非稳态、非线性的特点,提出一种基于EEMD应用于刀具磨损状态识别的方法。通过EEMD获取无模态混叠的IMF分量;通过敏感度评估算法从所有IMF分量中提取敏感的IMF;提取敏感IMF的能量作为支持向量机(SVM)分类器的输入,将刀具分成正常切削、中期磨损和严重磨损3种状态。通过比较EEMD与应用EMD等方法的分类准确率,确立了基于EEMD的方法在提取刀具磨损状态特征信息的优势。
Ensemble empirical mode decomposition（EEMD） is presented to alleviate the mode mixing problem occurring in EMD.Feature information of signal is revealed more accurately than with EMD,with helps of EEMD.According to unstable-state and non-linear characteristics of acoustic emission signals,an applied method for tool wear state identification based on EEMD is presented.The IMF components with no mode mixing can be obtained with EEMD.The sensitivity evaluation algorithm extracts sensitive IMF from all the IMF.The energy of the sensitive IMF is extracted as input of support vector machine（SVM） classifier,and the tool wear state is divided into three kinds of state：normal cutting,medium wear and severe wear.By comparing classification accurate rate of EEMD and applied EMD methods,the superiority of the proposed method based on EEMD is demonstrated in state recognition of tool wear.
出处
《传感器与微系统》
CSCD
北大核心
2012年第5期147-149,152,共4页
Transducer and Microsystem Technologies
基金
辽宁省重点实验室基金资助项目(LS2010117)
博士后启动基金资助项目(89017)
关键词
刀具磨损
状态识别
总体经验模态分解
经验模态分解
支持向量机
tool wear
state recognition
ensemble empirical mode decomposition（EEMD）
empirical mode decomposition（EMD）
support vector machine（SVM）
