TY - JOUR N2 - 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. L1 - http://www.czasopisma.pan.pl/Content/90317/PDF/Journal10178-VolumeXXII%20Issue2_03paper.pdf.pdf L2 - http://www.czasopisma.pan.pl/Content/90317 PY - 2015 IS - No 2 EP - 220 DO - 10.1515/mms-2015-0017 KW - power electronic circuit KW - fault classification KW - support vector data description KW - support vector machine A1 - Cui, Jiang PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation VL - vol. 22 DA - 2015[2015.01.01 AD - 2015.12.31 AD] T1 - Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method SP - 205 UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/90317 T2 - Metrology and Measurement Systems ER -