Improving Fault Isolation in DC/DC Converters Based with Bayesian Belief Networks

详细信息    查看全文

• 作者：Abbass Zein Eddine^** ; ^{abbass.zein-eddine@etu.univ-lehavre.fr" class="auth_mail" title="E-mail the corresponding author} ; Iyad Zaarour^* ; Francois Guerin^** ; ^{francois.guerin@univ-lehavre.fr" class="auth_mail" title="E-mail the corresponding author} ; Abbas Hijazi^* ; ^{abhijaz@ul.edu.lb" class="auth_mail" title="E-mail the corresponding author} ; Dimitri Lefebvre^** ; ^{dimitri.lefebvre@univ-lehavre.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fault Detection ; Isolation (FDI) ; DC/DC converter ; Bayesian Belief Network ; Observer design ; Bayesian Naive Classifier
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：5
• 页码：303-308
• 全文大小：989 K

文摘

This paper lies in the domain of Fault Detection and Isolation (FDI). A Bayesian Naïve Classifier (BNC) structure is selected and used as a first attempt to use Bayesian Belief Networks (BBNs) for DC/DC power converter FDI. In order to highlight the BNC capabilities, it is compared to the well known and used FDI method based on Proportional Observer (PO). This comparative study is based on real data collected from a Zero Volt Switch (ZVS) Full Bridge Isolated Buck converter.