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.

中文翻译：

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基于小波描述子的自加湿质子交换膜燃料电池故障诊断比较研究

故障诊断可以帮助延长燃料电池的寿命，并且已广泛用于外部加湿的燃料电池。然而，尽管自加湿燃料电池很重要，但故障检测仍然很少。本文探讨了各种基于小波的描述符，以识别自加湿堆栈中的水合水平。基于小波的技术是非侵入性的，可以同时进行时间和频率检查。因此，它们封装了可以用作健康监控功能的健康相关数据。这里使用的基于小波的特定技术是（i）通过基于小波的快速电化学阻抗谱（EIS）获得的阻抗，以及（ii）通过小波和小波包分解的小波能量变化。根据结果​​在脱水条件下的趋势以及这些趋势在不同电流和采样率下的鲁棒性进行比较。然后使用RReliefF（Regression-ReliefF）-基于回归的特征排名工具根据其预测能力对它们进行评估。基于定性和定量分析，在为自加湿燃料电池创建短期健康监测系统的背景下评估描述符。虽然小波能量的修改版本为区分故障提供了最佳结果，但快速EIS可以为分析更复杂的故障模式提供可能性，并且显示出更强大的功能。基于定性和定量分析，在为自加湿燃料电池创建短期健康监测系统的背景下评估描述符。虽然小波能量的修改版本为区分故障提供了最佳结果，但快速EIS可以为分析更复杂的故障模式提供可能性，并且显示出更强大的功能。基于定性和定量分析，在为自加湿燃料电池创建短期健康监测系统的背景下评估描述符。虽然小波能量的修改版本为区分故障提供了最佳结果，但快速EIS可以为分析更复杂的故障模式提供可能性，并且显示出更强大的功能。