This was then used to perturb the PEFC, with the corresponding AC (alternating current) voltage response being measured. Traditionally, the system response is converted to the frequency domain and analysed with Nyquist plots; however, in this case, the data was kept in the time domain for two main reasons. Firstly, all the information available in the Nyquist plots is also available in the AC voltage response of the system, albeit heavily convoluted. Secondly, conversion to the frequency domain is often time-consuming and computationally demanding; with the objective of diagnosing the system as quickly as possible, removing this stage of the process helped significantly. The AC voltage response was used directly as the diagnostic variable for the input to a 1-dimensional convolutional neural network (1D-CNN) which was trained with samples corresponding to flooding, drying, and normal operating states, producing a diagnostic accuracy of 100%. Despite this, a drawback of the methodology is that the summation of the perturbation signals increases the overall amplitude of the multifrequency signal. Typically, a perturbation amplitude of 5-10% of the DC current is used since excessive amplitudes alter the operation of the PEFC, while diminutive amplitudes are hidden in operating noise, reducing measurement accuracies .