Szczegóły

Tytuł artykułu

A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

Tytuł czasopisma

Metrology and Measurement Systems

Rocznik

2017

Numer

No 4

Autorzy publikacji

Wydział PAN

Nauki Techniczne

Abstrakt

<jats:title>Abstract</jats:title><jats:p>Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.</jats:p>

Wydawca

Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation

Data

2017

Identyfikator

ISSN 0860-8229

DOI

10.1515/mms-2017-0056

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