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Scientia Iranica
Article . 2012 . Peer-reviewed
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Scientia Iranica
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DBLP
Article . 2012
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Application of transform in fault diagnosis of power electronics circuits

Authors: Rongjie Wang; Yiju Zhan; Haifeng Zhou;

Application of transform in fault diagnosis of power electronics circuits

Abstract

AbstractA method based on S transforms and support vector machines was presented for fault diagnosis of power electronics circuits in which the S transform time-frequency analysis of the fault signal is used to extract the features corresponding to various faults. Then, fault types are identified through the pattern recognition classifier, based on SVM. The simulation results show that the proposed method can accurately diagnose faults and locate fault elements for power electronics circuits. It also has excellent performance for noise robustness and calculation complexity, thus, having good practical engineering value in the solution to fault problems for power electronics circuits.

Related Organizations
Keywords

Support vector machine, Pattern recognition classifier, Power electronics circuits, S transforms, Fault diagnosis

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Average
gold