Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method

• Autorzy: Cui J.

Identyfikatory

DOI

10.1515/mms-2015-0017

• Języki publikacji: EN

Abstrakty

EN

Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.

Słowa kluczowe

EN

power electronic circuits; fault classification; support vector data description; support vector machine (SVM)

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2015
• Tom: Vol. 22, nr 2
• Strony: 205--220
• Opis fizyczny: Bibliogr. 35 poz., fot., rys., tab., wykr., wzory

Twórcy

autor

Cui J.

• Nanjing University of Aeronautics and Astronautics, College of Automation Engineering, Nanjing City, Jiangsu Province, 211100, China

Bibliografia

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Uwagi

EN

This work was supported by the Fundamental Research Funds for the Central Universities (grant no. NS2014028) of China.