文章基本信息

标题：Improving Fault Isolation in DC/DC Converters Based with Bayesian Belief Networks
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作者：Abbass Zein Eddine ; Iyad Zaarour ; Francois Guerin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：5
页码：303-308
DOI：10.1016/j.ifacol.2016.07.130
语种：English
出版社：Elsevier
摘要：AbstractThis 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.
关键词：KeywordsFault DetectionIsolation (FDI)DC/DC converterBayesian Belief NetworkObserver designBayesian Naive Classifier