This paper focuses on developing a methodology to diagnose PEFC faults in a fast, on-line, and nonintrusive manner to maintain optimal operation and improve durability. The final objective is for these efforts to help realise the commercial ubiquity of PEFCs, providing a reliable and clean alternative energy source for use in mobile or stationary applications. Here we demonstrate that the Walsh function can be effectively used to minimise amplitude increases in multifrequency perturbations and that the diagnostics of faults in fuel cells using CNNs is highly accurate when trained on the same fuel cell. Although generalisation ability is limited when applied to a different fuel cell system, it improves significantly when a portion of training data from the same system is used.