Fault detection based on a robust one class support vector machine
Fault detection based on a robust one class support vector machine
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DOI：
10.1016/j.neucom.2014.05.035
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发表时间：
2014-12
期刊：
影响因子：
6
通讯作者：
Shen Yin;Xiangping Zhu;Chen Jing
中科院分区：
文献类型：
--
作者：
Shen Yin;Xiangping Zhu;Chen Jing
本文提出了一种基于鲁棒单类支持向量机（1-class SVM）的故障检测方法。1-类支持向量机是一般支持向量机的一种特殊变体，由于只需要正常数据进行训练，因此1类支持向量机被广泛用于异常检测。然而，实验表明，1类支持向量机是敏感的训练数据集中包含的离群值。为了科普这个问题，本文提出了一种鲁棒的1-class SVM。通过设计惩罚因子，鲁棒1类支持向量机可以有效抑制野值的影响。基于鲁棒1类支持向量机构造故障检测方案。仿真实例表明，鲁棒1-class SVM比一般1-class SVM有上级的性能，尤其是在训练数据集被野值破坏时，基于鲁棒1-class SVM的故障检测方案具有令人满意的性能。
A new fault detection scheme based on the proposed robust one class support vector machine (1-class SVM) is constructed in this paper. 1-class SVM is a special variant of the general support vector machine (SVM) and since only the normal data is required for training, 1-class SVM is widely used in anomaly detection. However, experiments show that 1-class SVM is sensitive to the outliers included in the training data set. To cope with this problem, a robust 1-class SVM is proposed in this paper. With the designed penalty factors, the robust 1-class SVM can depress the influences of outliers. Fault detection scheme is constructed based on the robust 1-class SVM. The simulation example shows that the robust 1-class SVM is superior to the general 1-class SVM, especially when the training data set is corrupted by outliers, and the fault detection scheme based on robust 1-class SVM presents satisfactory performances.