A Comparative Study of Wavelet‐based Descriptors for Fault Diagnosis of Self‐Humidified Proton Exchange Membrane Fuel Cells

Abstract Fault diagnosis can help extend the lifetime of fuel cells and has been widely explored for externally humidified fuel cells. However, fault detection in self‐humidified fuel cells is scarce, despite its importance. This paper explores various wavelet‐based descriptors for identifying hydration levels in self‐humidified stacks. Wavelet‐based techniques are non‐intrusive and provide concurrent examinations in time and frequency. Thus, they encapsulate health related data that can be used as health monitoring features. The specific wavelet‐based techniques used here are (i) impedance obtained through the wavelet‐based fast electrochemical impedance spectroscopy (EIS) and (ii) changes in wavelet energy through wavelet and wavelet packet decomposition. Results are compared based on their trends under dehydration conditions and the robustness of those trends across different currents and sampling rates. They are then assessed based on their predictive ability using RReliefF (Regression‐ReliefF)‐ a regression based feature ranking tool. Based on the qualitative and quantitative analysis, descriptors are assessed in the context of creating a short‐term health monitoring system for self‐humidified fuel cells. While a modified version of the wavelet energies provides the best results for differentiating faults, fast EIS opens the possibility for analysis of more complex fault modes and is shown to be more robust.