使用电化学噪声小波分析对聚合物膜燃料电池进行早期失效模式诊断通过三种方法进行失效模式诊断：电压噪声傅里叶变换、电压噪声小波变换、电化学阻抗
Proton exchange membrane fuel cell failure mode early diagnosis with wavelet analysis of electrochemical noise

M．A．Rubioa

K．Bethuneb  

A．Urquiaa

J．St－Pierreb

Abstract

A diagnostic method for the performance degradation of low temperature proton exchange membrane fuel cells is proposed． The method is based on the analysis of the cell electrochemical noise． Experimental noise data were collected for a range of air relative humidities and stoichiometries including conditions leading to water flooding， membrane dehydration and air starvation failure modes． Data were converted with a Fourier transform （frequency window averaging of the amplitude） and a wavelet transform （coefficients standard deviation）． Data were compared to impedance spectroscopy results． The method based on the wavelet transform was more sensitive． Cell states labeled by their air relative humidity and stoichiometry were correctly identified using a brute force algorithm by minimizing the Chebyshev distance between the actual and the calculated states． Independent and uniformly distributed random variations were added to experimental wavelet coefficients＇ standard deviations to define the calculated states．

化学计量比从1．7下降到1．6，电压都是下降5mV，电压噪声小波变换的响应和标准偏差在不同湿度下的相应竟然有差异。

对敏感性的测试一般在出现电压波动时就停下来了，后面继续降低时的响应倒是第一次看到。

湿度敏感性分析

低频各频段各种条件下的频谱，实现了在线通过特定频段小波变换和测试中断通过变频电化学阻抗谱的相互对应关系，从而判断失效模式。

Conclusions
The diagnosis of proton exchange membrane fuel cells was
explored by comparing Fourier and wavelet transform analyses
of cell voltage noise to electrochemical impedance data．
The main conclusions are：
The wavelet transform procedure is preferred because it is
suitable for real－time analysis and yields a relatively
greater sensitivity to operating condition changes （air
stoichiometry and relative humidity）．
The changes in the wavelet coefficient standard deviations
for selected noise frequencies are sufficient to reconstruct
the original fuel cell operating conditions state．

Future work will consider the following development
aspects：
Assess the suitability of the wavelet transform analysis with an enlarged fuel cell operating conditions state space by adding other relevant parameters， such as air contaminants， and by using the entire range investigated here （air starvation）． It is emphasized that failure modes that do not lead to permanent fuel cell degradation were initially considered to minimize resource use． From that standpoint， many air contaminants create reversible effects．
Develop and demonstrate a real－time diagnostic algorithm under dynamic conditions and for a larger set of failure modes．