One potential solution to the issue of requiring a large training dataset is to reduce the number of samples from each individual MEA. In this way, a few samples from a wide variety of different MEAs at different points in their life and different operating conditions could be used, such that any normally operating MEA will fit somewhere into this range. This would avoid having to train with data from every MEA that makes use of the model, provided that the initial testing data is taken from an extensive range of MEA ages and operating conditions. To test the concept of only including a small amount of new data from a different MEA, the train and test Figure shows how the accuracies of the models increase as a larger fraction of is added to the training set. When none of was used to train the models, a 73.0% accuracy was achieved with the DNN in comparison to the ~33% accuracies obtained with the 1D-CNN and SVMs (as previously described). However, when a larger proportion of is used to train the DNN, its accuracy only increases slightly, plateauing at ~90%. In contrast, although the 1D-CNN performs poorly when = 0, its diagnostic accuracy quickly improves as it gets trained on a larger amount of , reaching a maximum of 99.1%. The steep increase in accuracy when a small addition of is used to train the 1D-CNN is particularly appealing, suggesting that it could perform well for generalisation diagnosis, where a small amount of data from a variety of MEAs is used for training.