Bayesian network supervision on fault tolerant fuel cells

Luis A.M. Riascos, Fábio G. Cozman, Paulo E. Miyagi, Marcelo G. Simões

Research output: Contribution to conferenceConference Paper

18 Scopus citations

Abstract

In this paper, a supervisor system, able to diagnose different types of faults during the operation of a proton exchange membrane fuel cell (PEMFC), is introduced. The diagnosis is developed by applying Bayesian networks, which qualify and quantify the cause-effect relationship among the variables of the process. The fault diagnosis is based on the online monitoring of variables easy to measure in the machine such as voltage, electric current, and temperature. The fault effects are based on experiments on a fault tolerant fuel cell, which are reproduced in a fuel cell model. A database of fault records is constructed from the fuel cell model, improving the generation time and avoiding permanent damage to the equipment. © 2006 IEEE.
Original languageAmerican English
Pages1059-1066
Number of pages8
DOIs
StatePublished - 1 Dec 2006
Externally publishedYes
EventConference Record - IAS Annual Meeting (IEEE Industry Applications Society) -
Duration: 1 Dec 2006 → …

Conference

ConferenceConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
Period1/12/06 → …

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