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Fault detection based on a robust one class support vector machine
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DOI：10.1016/j.neucom.2014.05.035.png)
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Abstract
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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. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Support vector machines
Outliers
One class support vector machines
Fault detection
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