CAJ | 학술논문

提出一种基于核的局部边界Fisher判别(KLMFD)算法用核函数将故障特征数据映射到高维核空间,以每点与所有局部邻域点中最远同类数据点和最近异类数据点构成的点对来计算类内散度和类间散度,构建边界局部核Fisher判别函数,求出最优故障识别向量,然后利用该向量对测试特征数据进行故障诊断.转子故障诊断实验表明,对于多传感器振动特征融合信号,KMLFD算法的故障诊断效果最好,当选取合适参数时能完全识别故障类型.
제출일충기우핵적국부변계Fisher판별(KLMFD)산법용핵함수장고장특정수거영사도고유핵공간,이매점여소유국부린역점중최원동류수거점화최근이류수거점구성적점대래계산류내산도화류간산도,구건변계국부핵Fisher판별함수,구출최우고장식별향량,연후이용해향량대측시특정수거진행고장진단.전자고장진단실험표명,대우다전감기진동특정융합신호,KMLFD산법적고장진단효과최호,당선취합괄삼수시능완전식별고장류형.
In order to better identify the fault of rotor system, one new method based on kernel local-margin fisher discriminant (KLMFD) is proposed.In this methd, fualt feature data are mapped to high-dimensional space by kernel function, computed with-class scatter and between-class scatte based on the farthest congeneric data point and the re-cent heterogeneous data point of each point in all the local neighborhood, constructed kernel local magin fisher dis-criminant function, found optimal fault diagnosis vector. Then fault of new testing data are identified by this vector.The experiment showed, KLMFD algorithm had best effect in comparison to other manifold learning algorithm to the rotor fault diagnosis, and can fully identify fault type when selecting the appropriate parameters.