Fuel Cell aging monitoring and diagnosis are key-issues for scientists and industrials who intend to spread this technology. In the present work, we propose an efficient method that enables the extraction of valuable information which contains indicators on the aging of a studied PEM Fuel Cell (PEMFC). In this context, we investigate the possibilities offered by the Wavelet Leaders based Multifractal Analysis (WLMA), which is a suitable method to study the clustering variability of non-stationary fluctuating signals. A continuous aging test of 124 hours conducted under nominal operating conditions serves as data input for the development of our diagnosis method. Singularity spectra (SS) are calculated on the stack voltage signals at different periods of time. We observe that stack voltage over extended periods of operating time leads to different types of singularities, i.e., we find a narrow range of values of Hölder exponent h with non-zero fractal dimension D(h) according to the operating time of the FC. We conclude that this dynamic multifractality of the stack voltage can be considered as a pertinent tool for the monitoring of the FC aging state. Framework of the 'Decentralized energy production' project, directed by EFFICACITY, the French R&D Institute for urban energy transition.