基于自注意力机制和CNN融合的燃料电池故障诊断技术

施永; 黄宁; 谢缔; 姚继刚; 汪亮亮

doi:10.19912/j.0254-0096.tynxb.2024-0149

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 53-61. DOI: 10.19912/j.0254-0096.tynxb.2024-0149

基于自注意力机制和CNN融合的燃料电池故障诊断技术

施永¹, 黄宁¹, 谢缔², 姚继刚², 汪亮亮²

作者信息 +

FUEL CELL FAULT DIAGNOSIS TECHNIQUE BASED ON SELF-ATTENTION MECHANISM AND CNN FUSION

Shi Yong¹, Huang Ning¹, Xie Di², Yao Jigang², Wang Liangliang²

Author information +

文章历史 +

摘要

该文提出一种基于自注意力机制和卷积神经网络融合的燃料电池故障诊断技术,利用卷积神经网络对故障特征进行提取,通过自注意力机制自适应调节分类网络模型对关键特征的学习权重,减少特征冗余对分类准确性的影响,可显著提高燃料电池故障诊断的精度。经验证,该方法在水淹和膜干的故障诊断中准确率达到99.75%。

Abstract

In this paper, a fuel cell fault diagnosis technique is proposed based on the fusion of the self-attention mechanism and convolutional neural network,which uses a convolutional neural network to extract the fault features, and adaptively adjusts the learning weights for key features in the classification network model through the self-attention mechanism, to reduce the influence of feature redundancy on the classification accuracy. The method has been verified to achieve 99.75% accuracy in fault diagnosis for water flooding and membrane drying.

导出引用

施永, 黄宁, 谢缔, 姚继刚, 汪亮亮. 基于自注意力机制和CNN融合的燃料电池故障诊断技术[J]. 太阳能学报. 2025, 46(5): 53-61 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0149

Shi Yong, Huang Ning, Xie Di, Yao Jigang, Wang Liangliang. FUEL CELL FAULT DIAGNOSIS TECHNIQUE BASED ON SELF-ATTENTION MECHANISM AND CNN FUSION[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 53-61 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0149

中图分类号： TM911.4

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