基于切削声信号与优化SVM的刀具磨损状态监测  

Tool Wear Condition Monitoring Based on Cutting Sound Signal and Optimized SVM

作　　者：张锴锋[1,2] 袁惠群[1] 聂鹏[2] 

机构地区：[1]东北大学机械工程与自动化学院,沈阳110819 [2]沈阳航空航天大学机电工程学院,沈阳110136

出　　处：《振动．测试与诊断》2015年第4期727-732,800-801,共6页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51275081);国家自然科学基金重点资助项目(51335003);辽宁省科技创新重大专项基金资助项目(201303004);沈阳市科技攻关计划资助项目(F13-014-2-00))

摘　　要：提出了一种利用切削声实现刀具磨损状态多特征监测的方法。根据经验模态分解与Hilbert变换理论,提取切削声信号的内禀模态能量与不同频段的Hilbert谱能量作为监测信号的备选特征。采用支持向量机作为分类器,针对备选特征的有效筛选问题,利用多种群遗传算法对分类器的输入特征进行了优化,剔除备选特征中的干扰特征,利用多种群遗传算法对分类器的模型参数进行了优化。利用优化后的分类器对测试样本进行分类,并与优化前的分类结果进行了对比。结果表明,优化后分类器的分类性能得到了明显提升,该方法可以对刀具磨损状态进行有效识别。A method for realizing tool wear condition monitoring using multi-feature of the cutting sound is presented.Based on empirical mode decomposition and Hilbert transformation theories,the cutting sound signal is analyzed.The energies of intrinsic modes and Hilbert spectrum in different frequency ranges are extracted as candidatefeatures of the monitoring signal.To solve the feature selection problem,the sup-port vector machine is selected as the classifier,and the multiple population genetic algorithm is used to optimize its input features.Then,the interference features are eliminated from the candidate features.After the classifier parameters are also optimized with the multiple population genetic algorithm,the test samples are classified with the optimized classifier,and the performances of the classifiers before and after optimization are compared.The results show that the performance of the optimized classifier is significantly improved,and the method can be used effectively for identification of the tool wear condition.

关 键 词：经验模态分解 HILBERT变换 切削声 支持向量机 多种群遗传算法 

分 类 号：TH164[机械工程—机械制造及自动化]