Li-xiang Duan, Mengyun Xie, Tangbo Bai, Jinjiang Wang
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引用次数: 2

摘要

在机械故障诊断领域,故障样本往往难以获取,故障样本数量远远少于正常样本数量,导致数据集不平衡问题。针对不平衡数据集下的多分类问题,提出了一种基于马氏距离的支持向量数据描述(SVDD)和二叉树(BT)相结合的新模型。该方法的思想是采用二叉树将原始样本划分为一系列子集,然后通过SVDD描述目标的边界来构建分类器。该方法着重研究了:1)基于马氏距离的可分性度量。它代表了考虑了各类之间的不平衡程度和距离的可分度,并利用马氏距离的定义来考虑数据集各特征之间的关系,有助于确定二叉树的结构。2)使用SVDD训练分类器。根据二叉树的顺序选择目标类。该方法可以应用于具有不平衡数据集的多分类问题。为了验证该方法,利用不平衡转子的样品进行了实验。实验结果表明,该方法对非平衡数据集下的多分类问题具有更好的性能和更高的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Support vector data description for machinery multi-fault classification with unbalanced datasets
In mechanical fault diagnosis area, fault samples are often difficult to obtain, so the number of fault samples is far less than that of normal samples which leads to the unbalanced dataset issues. A novel model combining SVDD (Support Vector Data Description) and binary tree (BT) based on Mahalanobis distance is put forward to address the multi-classification problems under unbalanced datasets. The idea of the proposed method is to divide the original samples into a series of subsets by adopting binary tree, and then build classifier by describing the boundary of the target via SVDD. The proposed method has emphatically studied on: 1) Separability measure based on Mahalanobis distance. It represents the separability degree which takes the unbalanced degree and distance between each class into account, and takes the advantages of considering the relations among all the features of the datasets by the definition of Mahalanobis distance, it is helpful to determine the structure of the binary tree. 2) Train classifiers by using SVDD. Choose the target class according to the order of binary tree. The proposed method can be applied to multi-classification problems with unbalanced datasets issues. To validate this methodology, samples from unbalanced rotor are employed for experiment. Then, the experimental result compared with other methods is presented showing that the proposed methodology has a better performance and higher classification accuracy on multi-classification problems under unbalanced datasets.
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