提取有效的特征对高维数据的模式分类起着关键作用, 针对现有故障诊断方法故障特征维数过高 的问题, 文章提出了一种基于指数正则化零空间线性鉴别分析(exponential regularized null space linear discriminant analysis, ERNSLDA) 的故障诊断方法. 零空间线性判别分析已经在数据降维和特征提取上展现出良好的性能, 在文章中, 首先对类内样本矩阵进行正则化处理, 避免小样本问题, 其次对判别准则进行指数化处理. 所提方法集成了~NSLDA 和~RLDA 在模式识别上的优势, 有效地提高了故障诊断的精度, 通过发动机失火状态识别以及齿轮箱故障摸模拟试验验证了所提方法的有效性.

The extraction of effective recognition feature is important for high dimensional date for pattern recognition. In order to solve the problem of low accuracy and high feature dimension of fault feature for the present fault diagnosis methods. A new fault diagnosis algorithm based on exponential regularized null space linear discriminant analysis is proposed on this paper. Null space linear discriminant analysis (NSLDA) shows desirable performance, in this paper, the within class sample scatter is regularized at first in order to avoid the small sample size problem, then we utilize the matrix exponential to the discriminant criterion to enhance the performance. The proposed method integrated the advantages of NSLDA in dimension reduction and RLDA in pattern recognition and effectively improved the accuracy of fault diagnosis. The validity of the proposed method is verified by the instance of the fault diagnosis of a gearbox and engine misfire.