质子交换膜燃料电池
稳健性(进化)
卷积神经网络
计算机科学
电压
可靠性(半导体)
耐久性
含水量
膜
人工智能
数据挖掘
模式识别(心理学)
算法
燃料电池
工程类
化学
化学工程
数据库
电气工程
物理
基因
量子力学
功率(物理)
生物化学
岩土工程
作者
Heng Zhang,Zhongyong Liu,Weilai Liu,Lei Mao
出处
期刊:Energies
[MDPI AG]
日期:2022-06-09
卷期号:15 (12): 4247-4247
被引量:2
摘要
In existing proton exchange membrane fuel cell (PEMFC) applications, improper membrane water management will cause PEMFC performance decay, which restricts the reliability and durability of PEMFC systems. Therefore, diagnosing improper water content in the PEMFC membrane is the key to taking appropriate mitigations to guarantee its operating safety. This paper proposes a novel approach for diagnosing improper PEMFC water content using a two-dimensional convolutional neural network (2D-CNN). In the analysis, the collected PEMFC voltage signal is transformed into 2D image data, which is then used to train the 2D-CNN. Data enhancement and pre-processing techniques are applied to PEMFC voltage data before the training. Results demonstrate that with the trained model, the diagnostic accuracy for PEMFC membrane improper water content can reach 97.5%. Moreover, by comparing it with a one-dimensional convolutional neural network (1D-CNN), the noise robustness of the proposed method can be better highlighted. Furthermore, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to visualize the feature separability with different methods. With the findings, the effectiveness of using 2D-CNN for diagnosing PEMFC membrane improper water content is explored.
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