Bayesian network supervision on fault tolerant fuel cells

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

Resultado de la investigación: Contribución a una conferenciaArtículo de conferencia

18 Citas (Scopus)

Resumen

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.
Idioma originalInglés estadounidense
Páginas1059-1066
Número de páginas8
DOI
EstadoPublicada - 1 dic 2006
Publicado de forma externa
EventoConference Record - IAS Annual Meeting (IEEE Industry Applications Society) -
Duración: 1 dic 2006 → …

Conferencia

ConferenciaConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
Período1/12/06 → …

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    Riascos, L. A. M., Cozman, F. G., Miyagi, P. E., & Simões, M. G. (2006). Bayesian network supervision on fault tolerant fuel cells. 1059-1066. Papel presentado en Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), . https://doi.org/10.1109/IAS.2006.256655