In this paper, a novel black-box model approach was developed to diagnose PEM fuel cell faults, involving the use of multifrequency Walsh function AC perturbation signals to capture convoluted information on their SoH. The Walsh function was employed to minimise the overall amplitude of the perturbation signals, improving the signal-to-noise ratio while ensuring that the PEFC is not damaged by high amplitude perturbations. The corresponding voltage responses were then used directly as the diagnostic inputs to a DNN, 1D-CNN, linear-SVM, and RBF-SVM to compare their effectiveness at classifying the SoH; all models were able to effectively diagnose normal, drying, and starvation conditions of an individual MEA, with the 1D-CNN and SVMs achieving 100% diagnostic accuracy.