@inproceedings{a24f6fa448454120863e9207ecbd0f23,
title = "SOFC modelling based on discrete Bayesian network for system diagnosis use",
abstract = "We propose in this paper a diagnosis method that is aimed to detect and isolate SOFC system fault by using the FC stack as a sensor. A discrete Bayesian network (BN) was established to illustrate the input-output causal relations of the stack. In order to examine the generalizability of the network structure, the BN was parameterized to fit the experimental data from two different SOFC systems. The models showed reasonable accuracy of state estimation for 6 operating variables. Finally, the BN model was experimented for diagnosing a specified system fault.",
keywords = "Bayesian network, conditional probability, fault diagnosis, maximum likelihood, SOFC",
author = "K. Wang and M.C. Pera and D. Hissel and {Yousfi Steiner}, N. and Antti Pohjoranta and S. Pofahl",
year = "2012",
doi = "10.3182/20120902-4-FR-2032.00118",
language = "English",
isbn = "978-3-902823-24-3",
series = "IFAC Proceedings Volumes",
publisher = "Elsevier",
number = "21",
pages = "675--680",
editor = "Maurice Fadel and St{\'e}phane Caux",
booktitle = "8th Power Plant and Power System Control Symposium",
address = "Netherlands",
note = "8th Power Plant and Power System Control Symposium, PPPSC 2012, PPPSC 2012 ; Conference date: 02-09-2012 Through 05-09-2012",
}
