断层(地质)
质子交换膜燃料电池
特征选择
介电谱
电阻抗
Softmax函数
计算机科学
工程类
算法
人工神经网络
人工智能
化学
燃料电池
电化学
电气工程
地质学
物理化学
地震学
化学工程
电极
作者
Jianfeng Lv,Zhonghai Yu,Guanghui Sun,Jianxing Liu
标识
DOI:10.1016/j.jpowsour.2023.233815
摘要
Electrochemical Impedance Spectroscopy (EIS) serves as a valuable tool for analyzing the health of Proton Exchange Membrane Fuel Cell (PEMFC). However, the practical application of EIS-based fault diagnosis algorithms continues to face challenges, including time-consuming EIS measurements, inefficient Equivalent Circuit Model (ECM) parameter estimation, and limited generalization capability of fault diagnosis models. To enhance the utility of the EIS-based fault diagnosis methods, this paper develops a new deep-learning-based PEMFC fault diagnosis framework, along with a frequency selection method. Specifically, a Parameter Estimation Unit (PEU) is introduced to derive the ECM parameters directly from EIS measurements. A Frequency Selection Unit (FSU) based on the Gumbel-Softmax trick is proposed to enable the identification of the optimal frequency solution to maximize fault diagnosis performance with a predetermined number of measured frequency points. Furthermore, a Feature Augmentation Unit (FAU) is proposed to generate robust diagnosis features based on ECM parameters, thus reducing the influence of non-fault operations on fault diagnosis results. Experiments based on real EIS data demonstrate the effectiveness of the proposed algorithm. With these innovations, the proposed framework could significantly improve the efficiency and accuracy of fault diagnosis in PEMFC.
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