Fault Diagnosis for Fuel Cell Based on Naive Bayesian Classification

Abstract

Many kinds of uncertain factors may exist in the process of fault diagnosis and affect diagnostic results. Bayesian network is one of the most effective theoretical models for uncertain knowledge expression and reasoning. The method of naive Bayesian classification is used in this paper in fault diagnosis of a proton exchange membrane fuel cell (PEMFC) system. Based on the model of PEMFC, fault data are obtained through simulation experiment, learning and training of the naive Bayesian classification are finished, and some testing samples are selected to validate this method. Simulation results demonstrate that the method is feasible.

 DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3695