CAJ | 학술논문

针对现有流形学习理论用于旋转机械故障诊断存在识别精度不高的问题,提出基于有监督不相关局部 Fish-er判别分析(Supervised Uncorrelated Local Fisher Discriminant Analysis,SULFDA)的新型故障诊断方法。首先构造全面表征不同故障特征的时频域特征集,再利用有监督不相关局部 Fisher判别分析将高维时频域故障特征集化简为区分度更好的低维特征矢量,并输入到 K-近邻分类器中进行故障模式辨识。有监督不相关局部 Fisher 判别分析在类标签指导下最小化同类流形的离散度并最大化异类流形的离散度来实现类判别,还施加了不相关约束条件使所提取的特征统计不相关,提高了针对旋转机械的故障诊断精度。深沟球轴承故障诊断实验验证了该方法的有效性。
침대현유류형학습이론용우선전궤계고장진단존재식별정도불고적문제,제출기우유감독불상관국부 Fish-er판별분석(Supervised Uncorrelated Local Fisher Discriminant Analysis,SULFDA)적신형고장진단방법。수선구조전면표정불동고장특정적시빈역특정집,재이용유감독불상관국부 Fisher판별분석장고유시빈역고장특정집화간위구분도경호적저유특정시량,병수입도 K-근린분류기중진행고장모식변식。유감독불상관국부 Fisher 판별분석재류표첨지도하최소화동류류형적리산도병최대화이류류형적리산도래실현류판별,환시가료불상관약속조건사소제취적특정통계불상관,제고료침대선전궤계적고장진단정도。심구구축승고장진단실험험증료해방법적유효성。
Facing on the crucial problem that the fault diagnosis accuracy of current manifold learning theories for rotating ma-chinery is not high enough,a novel fault diagnosis method based on Supervised Uncorrelated Local Fisher Discriminant Analy-sis (SULFDA)is proposed in this paper.The time-frequency domain feature set is first constructed to completely characterize the property of each fault.Then,SULFDA is introduced to automatically compress the high-dimensional time-frequency do-main fault feature sets of training and test samples into the low-dimensional eigenvectors with better discrimination.Finally, the low-dimensional eigenvectors of training and test samples are input into K-nearest neighbors classifier (KNNC)to carry out fault identification.SULFDA achieves good discrimination ability by minimizing the within-manifold scatter and maximizing the between-manifold scatter under the supervision of class labels.Also,an uncorrelated constraint is put on SULFDA to make the extracted features statistically uncorrelated.Therefore,SULFDA improves the fault diagnosis accuracy for rotating machine. The fault diagnosis experiment on deep groove ball bearings demonstrated the effectivity of proposed fault diagnosis method.