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 nonintrusive 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 con-ditions 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.

A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

“…One of the most studied diagnostic techniques involves measuring the individual stack voltage and accompanying the voltage difference between them and the time evolution [142,143]. Other techniques involve more invading methods such as Impedance Spectroscopy (EIS) [144,145], while others opt for more model-based techniques through the use of residuals and hysteresis windows [91,145,146] or even datadriven [147,148] and signal processing tools [149][150][151][152][153][154].…”

Application of hypergraph theory in the analysis of the failure propagation and diffusion behaviour of machining centre

“…The analysis method mainly includes statistical analysis, signal processing, and artificial intelligence-based methods. [19][20][21] Given that failure diagnosis does not need to understand the analytical model of the system, this process can be realised only by using measurable signal analysis or direct reasoning based on numerous sampling and historical data. This method, which disregards the failure mechanism analysis, cannot establish a one-toone correspondence, thereby often resulting in inaccurate failure analysis results.…”

Fault Detection Methods Suitable for Automotive Applications in Proton Exchange Fuel Cells

“…The estimation of online parameters for EIS was recently studied in [20]. The impedance obtained from EIS was further analyzed as wavelet energy using wavelet and wavelet packet decomposition [21]. The data obtained using EIS was converted to the frequency domain using the Morlet wavelet [22].…”