Construction of multi-class classifiers by Extreme Learning Machine based one-class classifiers

Gautam, Chandan; Tiwari, Aruna; Ravindran, Sriram

doi:10.1109/ijcnn.2016.7727445

Cited by 3 publications

(1 citation statement)

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“…After the unknown fault is detected, a multi-class classifier is required for known state classification. There are three strategies for handling the problem of multi-class classification [49]: (1) The main contributions of this paper are summarized below .…”

Section: Introductionmentioning

confidence: 99%

Mechanical fault diagnosis of high-voltage circuit breakers using multi-segment permutation entropy and a density-weighted one-class extreme learning machine

Chen

Wan

2020

Meas. Sci. Technol.

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Condition monitoring for high-voltage circuit breakers (HVCBs) is of great significance for the safety of power grids. Based on machine-learning methods, most relevant studies have contributed significantly to improving the classification accuracy of known states. However, these studies have neglected the detection of unknown faults. In this study, a new one-class classifier, called a density-weighted one-class extreme learning machine (DW-OCELM), was proposed to detect unknown faults of HVCBs. The DW-OCELM determines the classification boundary considering data distribution by introducing the notion of density weight, such that samples located in low-density regions are more likely to be separated, improving detection performance. On this basis, a multi-class classifier was developed based on the homogeneous combination of multiple DW-OCELMs to classify known states. In addition, the proposed classifiers were trained based on multi-segment permutation entropy calculated from vibration signals. Experiments on a 35 kV HVCB demonstrated that the proposed methods outperformed other state-of-the-art techniques.

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Section: Introductionmentioning

confidence: 99%