Fault diagnosis of proton exchange membrane fuel cell using multiple convolutional neural networks with multi-scale attention mechanism

Jiang, Zhe, Yang, Bo, Zheng, Ruyi, Hou, Yitong, Li, Hongbiao, Gao, Dengke, Guo, Zhengxun and Jiang, Lin ORCID: 0000-0001-6531-2791 (2025) Fault diagnosis of proton exchange membrane fuel cell using multiple convolutional neural networks with multi-scale attention mechanism INFORMATION SCIENCES, 720. 122524-. ISSN 0020-0255, 1872-6291

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Abstract

To enhance the accuracy and robustness of fault diagnosis in proton exchange membrane fuel cells (PEMFCs), this study proposes a hybrid fault diagnosis model based on stacking ensemble learning. This model integrates multiple convolutional neural networks with a multi-scale attention mechanism (Stacking-MCNN-MSA). In the proposed model, MCNN is utilized to extract data features. A multi-head self-attention (MSA) mechanism is then applied to assign appropriate weights to these features. This process emphasizes critical information while suppressing noise. Subsequently, the MCNN-MSA component acts as the base learner. The predictions from the base learner are fed into the meta-learner to obtain the final fault diagnosis results. The research commences with data preprocessing, which involves crucial steps such as data noise reduction and data expansion. After that, the Stacking-MCNN-MSA model is constructed and evaluated through simulation experiments. Its performance is compared with that of alternative algorithms. The results demonstrate that the proposed model achieves high diagnostic accuracy under both original and noisy data conditions. Notably, after data expansion, the model attains a diagnostic accuracy of 98.67 %. These findings validate the effectiveness of the Stacking-MCNN-MSA model and provide a solid foundation for its practical application in PEMFC fault diagnosis.

Item Type:	Article
Uncontrolled Keywords:	Proton exchange membrane fuel cells, Fault diagnosis, Multiple convolutional neural networks, Stacking ensemble learning, SimuNPS
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	11 Aug 2025 07:35
Last Modified:	16 Jun 2026 20:24
DOI:	10.1016/j.ins.2025.122524
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3194001
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