机床与液压
機床與液壓
궤상여액압
MACHINE TOOL & HYDRAULICS
2013年
17期
47-52
,共6页
刀具磨损状态监测%双谱分析%奇异值分解%最小二乘支持向量机
刀具磨損狀態鑑測%雙譜分析%奇異值分解%最小二乘支持嚮量機
도구마손상태감측%쌍보분석%기이치분해%최소이승지지향량궤
Tool wear condition monitoring%Bispectrum analysis%Singular value decomposition%Least squares support vector machine
针对刀具磨损声发射信号的非线性、非平稳特性，提出一种基于双谱奇异值分解的刀具磨损特征提取方法。对刀具不同磨损阶段的声发射信号进行双谱分析，构造初始特征向量矩阵，然后对初始特征向量矩阵进行奇异值分解，计算奇异谱，将奇异谱作为刀具磨损特征向量，利用最小二乘支持向量机对刀具磨损状态进行识别。实验结果表明：所提取的特征可以很好地反映刀具的磨损状态，最小二乘支持向量机更适于在小样本下实现刀具磨损状态的识别，与神经网络识别方法相比具有更高的识别率。
針對刀具磨損聲髮射信號的非線性、非平穩特性，提齣一種基于雙譜奇異值分解的刀具磨損特徵提取方法。對刀具不同磨損階段的聲髮射信號進行雙譜分析，構造初始特徵嚮量矩陣，然後對初始特徵嚮量矩陣進行奇異值分解，計算奇異譜，將奇異譜作為刀具磨損特徵嚮量，利用最小二乘支持嚮量機對刀具磨損狀態進行識彆。實驗結果錶明：所提取的特徵可以很好地反映刀具的磨損狀態，最小二乘支持嚮量機更適于在小樣本下實現刀具磨損狀態的識彆，與神經網絡識彆方法相比具有更高的識彆率。
침대도구마손성발사신호적비선성、비평은특성，제출일충기우쌍보기이치분해적도구마손특정제취방법。대도구불동마손계단적성발사신호진행쌍보분석，구조초시특정향량구진，연후대초시특정향량구진진행기이치분해，계산기이보，장기이보작위도구마손특정향량，이용최소이승지지향량궤대도구마손상태진행식별。실험결과표명：소제취적특정가이흔호지반영도구적마손상태，최소이승지지향량궤경괄우재소양본하실현도구마손상태적식별，여신경망락식별방법상비구유경고적식별솔。
In view of the nonlinear and non-stationary characteristics of acoustic emission signal of tool wear,a feature extraction method based on bispectrum singular value decomposition was proposed. The bispectrum analysis method was used to decompose the collected acoustic emission signals that reflecting the different tool wear stage,the initial feature vector matrix was constructed. Then u-sing initial feature vector matrix,the singular spectrum was calculated by the method of singular value decomposition. Least squares support vector machine (LS-SVM)was selected to identify the tools wear state. The identification result shows that the bispectrum fea-ture can better reflect the tool wear state. LS-SVM is efficient,feasible and superior to neural network,and it has a higher identifica-tion rate.
