In 2013, to improve the issue of generalisation, Shao et al. used an ANN ensemble to identify four different faults associated with the cooling and reactant delivery systems . As proposed by Hansen and Salamon, ANN ensembles consist of multiple sub-ANNs which are trained separately before combining their outputs to form an ensemble output . This, therefore, improves their generalisation capability, since each sub-ANN can be trained on a different physical variable of the PEFC, preventing overfitting to any specific variable. When testing its performance, the ANN ensemble achieved a diagnostic accuracy of 93.24%, representing a significant improvement over the accuracies of the sub-ANNs alone, which ranged in accuracy from 75.24% to 85.62%. Support vector machines (SVMs) are a more recently developed subset of machine learning algorithms which formulate a hyperplane in a multidimensional space and classify data depending on which side of the hyperplane it lies . It was applied to PEFC fault diagnosis in 2016 by Li et al., who developed an on-line platform to predict SoH in real time based on the cell voltage profiles of cells within a stack . Attaining a global classification accuracy of 84.98%, it was concluded that the main reason for reductions in accuracy was due to the time delay of the on-line model. Furthermore, since the model was pre-trained on laboratory data, it did not account for the ageing effect of the PEFC; long-term performance degradation could cause the cell voltages to become too dissimilar from those used to train the model, reducing diagnostic accuracy.