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\begin{document}

\title{Power Electronic Circuit Fault Diagnosis Method Based on GADF and
Channel Split Residual Network}



\author[1]{Yuanyuan Jiang}%
\author[1]{Jinyang Xie}%
\affil[1]{Anhui University of Science and Technology}%


\vspace{-1em}



  
  \date{May 07, 2024}


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\begin{abstract}
\textbf{Power electronic circuits play an extremely important role in
industrial systems. Parametric failure of components in these circuits
are extremely prone to occur, which can easily evolve into catastrophic
failures with time. Hence fault diagnosis of circuits can avoid
catastrophic failures. In this paper, we propose a channel segmentation
residual network (CSRN) for circuit fault diagnosis, in which Gram
angular difference filed (GADF) is used to convert the original fault
signal into a two-dimensional feature map for input into the CSRN. The
channels of the input feature map are distinguished by selecting the
main working channels through a channel segmentation mechanism and a
Darwin selection mechanism for feature retention and deletion
operations. The diagnostic performance of the proposed method is
verified on the DC-DC circuit fault dataset, and the results show that
the proposed CSRN achieves 97.92\% fault classification accuracy for
this circuit, which is 1.98\%, 13.34\% and 6.67\% higher than other
models, respectively.}%
\end{abstract}%



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\textbf{Hosted file}

\verb`GADF.docx`  available at \url{https://authorea.com/users/525800/articles/596039-power-electronic-circuit-fault-diagnosis-method-based-on-gadf-and-channel-split-residual-network}

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