中文摘要：

针对有标记故障样本不足和故障特征集维数过高的问题，提出基于正交半监督局部 Fisher 判别分析（Orthogonal semi-supervised local Fisher discriminant analysis, OSELF）的故障诊断方法。所提出的OSELF能够充分地利用蕴含于无标记故障样本中的故障信息，避免了因有标记故障样本不足引起的过学习问题,同时采用正交迭代方式求解最优正交映射矩阵，克服现有方法无法得到正交映射矩阵的不足。正交映射矩阵的基矢量统计不相关，可有效地提高所得低维特征矢量的可辨识性。通过正交映射矩阵对故障样本集和新增样本进行维数约简，并将维数约简的结果输入粗糙优化k最近邻分类器（Coarse to fine k nearest neighbor classifier, CFKNNC）进行学习训练和故障识别。所提方法集成了OSELF在维数约简和CFKNNC在模式识别的优势，有效地提高了故障诊断的精度。通过齿轮箱故障模拟试验验证了该方法的有效性。

英文摘要：

Fault diagnosis method based on orthogonal semi-supervised local Fisher discriminant analysis（OSELF） is proposed, aiming to solve the problem of inadequate number of labeled fault samples and high dimensionality of the feature set. A new dimensionality reduction method named OSELF is proposed combining orthogonal iteration algorithm with semi-supervised local Fisher discriminant analysis（SELF）, which can effectively utilize the fault information supported by the labeled and unlabeled fault samples to embed the fault samples into the low-dimensional subspace without the over-fitted problem. The basis vectors of the orthogonal projection matrix are statically uncorrelated, and the discriminations of the obtained low-dimensional fault feature vectors are improved. Then the low-dimensional fault samples are fed into coarse to fine k nearest neighbor classifier（CFKNNC） to recognize the fault type. The proposed method integrated the advantages of OSELF in dimension reduction and CFKNNC 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.