Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote
Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote
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DOI：
10.3390/electronics9111952
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发表时间：
2020-11
期刊：
影响因子：
2.9
通讯作者：
Xian Du;Jingyang Yan;Rui Ma
中科院分区：
文献类型：
--
作者：
Xian Du;Jingyang Yan;Rui Ma
高维小样本的故障分类是一个挑战，特别是对于非线性和非高斯的制造过程。本文提出了一种基于相似度的特征选择和子空间邻域投票的方法来解决这个问题。为了捕捉不规则时间序列数据中的动态性、非线性和非高斯性，提取、选择和堆叠规则矩阵中的高阶谱特征和分形维数特征。为了解决小样本的问题，所有标记的故障数据用于特定故障类型的相似性决策。新数据与所有故障类型之间的距离在其特征子空间中计算。新的数据被分类到最近的故障类型的多数概率投票的距离。同时，从各个测量变量中选择的特征指示故障的原因。所提出的方法进行评估的一个公开的基准的真实的半导体蚀刻数据集。实验证明，利用高阶谱特征和分维特征，该方法可以达到84%以上的故障识别准确率。所得到的特征子空间可以用于将任何新的故障数据与每种故障类型的指纹特征子空间进行匹配，从而可以查明制造过程中故障的根本原因。
The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process.