Fault Identification in Fuel Cells Based on Bayesian Network Diagnosis

This paper considers the effects of different types of faults on a model of a proton exchange membrane fuel cell (PEMFC). Using databases (which record the effects of the faults) and probabilistic methods (such as the Bayesian-Score and Markov Chain Monte Carlo), a graphical-probabilistic model for fault diagnosis is constructed. The graphical structure 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 graphicalprobabilistic model) is used to execute the diagnosis of fault causes in the PEMFC based on observed effects. These effects can be monitored through sensors in the machine.