The use of neural networks in fuel cell diagnostics has increased in the last 15 years or so. In 2010, Yousfi Steiner et al. attempted to identify water management issues in PEFCs using a recurrent neural network (RNN), whereby the outputs of some neurons are fed back to previous layers, facilitating the storage of time-series data , . When a fault occurred in the PEFC, its output deviated from that of the RNN, generating a residual and allowing the fault state to be detected. Although the RNN modelled the system well, its ability to be generalised to other PEFC systems was significantly reduced since it was fitted too heavily to the exact PEFC used in the experiments.