To test the generalisation capability of the models, an additional identical MEA was fabricated, and the fault procedure was repeated to obtain a further 200 voltage responses for normal, drying, and starvation states. Thus far, the voltage responses were all taken from the same MEA at a similar point in its life; as the PEFC is operated over its lifespan, its performance will change accordingly, potentially altering the way it responds to the multifrequency perturbation To test the generalisation performances of the algorithms, they were independently trained on D 1 and tested on D 2 to check if they could predict the SoH of an MEA that the model had never 'seen' before. The diagnostic accuracies can be seen in Figure , indicating that the DNN outperformed the other models. Although the DNN was not able to fit as closely to the original data initially, this improved its generalisation capability, predicting 72% of the unseen data correctly. Conversely, when tested on D 2 , the 1D-CNN and SVMs only predicted ~33% of the unseen data correctly. Since there are three target classes, this accuracy indicates that the models were not able to distinguish between any of the fault states between the two datasets.