Nested SVDD in DAG SVM for induction motor condition monitoring
Nested SVDD in DAG SVM for induction motor condition monitoring
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DOI：
10.1016/j.engappai.2018.02.019
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Slaheddine Zgarni;H. Keskes;A. Braham
中科院分区：
文献类型：
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作者：
Slaheddine Zgarni;H. Keskes;A. Braham
目前，多类分类被认为是感应电机状态监测问题的主导技术，支持向量机可以准确有效地进行。标准的多类支持向量机（MSVM）的方法包括在构建一个最佳的决策超平面最大限度地提高训练数据之间的分离裕度。然而，相对少量的离群值会明显降低经典MSVM的性能，并会对判决边界和裕度计算产生影响。为了克服这一缺点，通常采用基于超球面决策边界的支持向量数据描述（SVDD）。本文的创新之处是引入一个新的扩展MSVM转子断条故障诊断嵌入SVDD在经典的多类SVM。实验结果证明了所提出的方法的优点与100%的分类率，这是高于任何标准MSVM方法实现的准确率。
Nowadays, multiclass classification is considered as the leading technique on the issue of condition monitoring in induction motor which can be performed accurately and efficiently by support vectors machines. The standard multiclass SVM (MSVM) approaches consists in constructing an optimal decision hyperplane maximizing the margin of separation between the training data. However, relatively small number of outliers can obviously reduce the performance of the classical MSVMs and would have an impact on the decision boundary and the margin calculation. Support Vector Data Description (SVDD) based on hyper-spheres decision boundary is often performed to overcome this drawback. The originality of this paper is to introduce a new extension of the MSVM for broken rotor bar fault diagnosis by embedding the SVDD in the classical multiclass SVM. The experimental results prove the merit of the proposed approach with a classification rate of 100%, which is higher than any accuracy rate achieved by standard MSVM approaches.