Although these results suggest that it is necessary to train the models using data from both MEAs, this is not ideal as, for the model to predict the SoH of any generic MEA, it's possible it would require previous data from the same MEA (though it could also be the case that training data containing a sufficiently large number and variation of MEAs would be able to classify new MEAs that are outside of the training data set. This is impractical for several reasons, including the excessive amount of data required to train the models, and the fact that, if an MEA changes unexpectedly during its lifetime, the model may no longer be able to predict its SoH, though more extensive investigations are required at this time to confirm this. Furthermore, the experimental results were obtained at the specified operating conditions in Table ; if the same MEA was operated at a different current density for example, there is no guarantee that the model will perform successfully at conditions that it was not trained with. This highlights the main limitation of black-box models, being their dependence on a good quality historical dataset, which is often difficult or impossible to obtain.