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

Cui J.

Metrology and Measurement Systems

2015

Vol. 22, nr 2

205--220

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.

Speaker recognition based on the combination of GMM and SVDD

Zhou Y., Zhang X., Wang J., Gong Y., Zhou Y.

Przegląd Elektrotechniczny

2011

R. 87, nr 3

329-332

Scare-level combination of subsystems can yield significant performance gains over individual subsystems in speaker recognition. A novel speaker verification method based on support vector data description (SVDD) is proposed to remedy the defect of Gaussian mixture model (GMM) to same extent, and then using the theory of multiple classifier systems (MCS),a new speaker recognition system based on the combination of GMM and SVDD is proposed. Experiments on TlMIT speech database show that the GMM-SVDD model fully utilizes the complementarities of GMM and SVDD to improve the performance obviously in speaker verification, closed-set speaker identification and speaker recognition.

Zaproponowano nowa metodę rozpoznawania głosu bazującą na systemie SVDD jako alternatywę dla modelu GMM. Następnie wykorzystując teorię wielokrotnego systemu klasyfikacji MCS zaproponowano wykorzystanie połączenia systemów GMM i SVDD. Eksperymenty potwierdziły że nowy model GMM-SVOO umożliwia ulepszonę rozpoznawanie głosu.