A Bayesian network fault diagnostic system for proton exchange membrane fuel cells

This paper considers the effects of different types of faults on a proton exchange membrane fuel cell model (PEMFC). Using databases (which record the fault effects) and probabilistic methods (such as the Bayesian-Score and Markov Chain Monte Carlo), a graphical–probabilistic structure for fault diagnosis is constructed. The graphical model defines the cause-effect relationship among the variables, and the probabilistic method captures the numerical dependence among these variables. Finally, the Bayesian network (i.e. the graphical–probabilistic structure) is used to execute the diagnosis of fault causes in the PEMFC model based on the effects observed.