计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2010年
14期
235-237
,共3页
特征提取%故障诊断%支持向量机%多类分类
特徵提取%故障診斷%支持嚮量機%多類分類
특정제취%고장진단%지지향량궤%다류분류
feature selection%fault diagnosis%Support Vector Machine(SVM)%multi-fault classifier
针对故障诊断中数据存在噪声和高维的缺点,使用一种快速特征提取方法对故障数据进行降维,该方法以特征信号的均值和方差作为其权重衡量的依据.利用支持向量机的模式分类功能,构造了基于特征提取的多故障分类器.实例表明,在保证诊断效果的情况下,该方法实现了数据降维.降低了运算复杂度.
針對故障診斷中數據存在譟聲和高維的缺點,使用一種快速特徵提取方法對故障數據進行降維,該方法以特徵信號的均值和方差作為其權重衡量的依據.利用支持嚮量機的模式分類功能,構造瞭基于特徵提取的多故障分類器.實例錶明,在保證診斷效果的情況下,該方法實現瞭數據降維.降低瞭運算複雜度.
침대고장진단중수거존재조성화고유적결점,사용일충쾌속특정제취방법대고장수거진행강유,해방법이특정신호적균치화방차작위기권중형량적의거.이용지지향량궤적모식분류공능,구조료기우특정제취적다고장분류기.실례표명,재보증진단효과적정황하,해방법실현료수거강유.강저료운산복잡도.
One method of fast feature selection is used in the high dimensional and noisy data of fault diagnosis to reduce the number of its attributes,according to the mean and the square of each feature of data.In this paper,a multi-fault classifier is de signed using the technology of Support Vector Machine(SVM) which has the function of pattern classification,based on feature extraction.The example shows that dimensionality reduction and computing complexity reduction are realized by using this method on the basis of achieving good performance of the multi-fault classifier.
