局部均值分解在刀具故障诊断中的应用  

Application of Local Mean Decomposition in Tool Fault Diagnosis

作　　者：聂鹏[1] 高辉[1] 陈彦海[1] 李正强[1] 董慧[1] 

机构地区：[1]沈阳航空航天大学机电工程学院,辽宁沈阳110136

出　　处：《北京理工大学学报》2012年第11期1125-1128,1133,共5页Transactions of Beijing Institute of Technology

基　　金：辽宁省重点实验室基金资助项目(LS2010117)

摘　　要：为有效监测刀具磨损状态,提出一种基于局部均值分解的刀具故障诊断方法.将声发射信号自适应地分解为一系列乘积函数,选取包含主要故障信息的前8个乘积函数分量,获得每个乘积函数分量的平均能量,并组成特征向量.分别提取正常切削、中期磨损和严重磨损三种状态下的特征向量,利用频带能量的变化识别刀具磨损特征.实验结果表明,随着刀具的磨损,各乘积函数分量平均能量增加,并且在高频部分增加显著,该方法可以有效应用在刀具故障诊断中.A tool wear fault diagnosis method based on local mean decomposition （LMD） was proposed to effectively monitor the cutting tool condition. By using LMD, acoustic emission signals would be adaptively decomposed into a series of product functions （PF）. Average energy of each PF was extracted from the first 8 product functions containing main fault information, which was represented as the feature vector. The cutter wear characteristics can be recognized according to the variance contribution of the parameters of feature vector, which could be obtained from normal, medium and severe wear states, respectively. Experimental results show that the average energy of PF increases with the increment of tool wear. Especially, the average energy in high frequency markedly increases. Test results also have verified the effectiveness of the presented method.

关 键 词：局部均值分解 刀具 故障诊断 声发射 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]