Work

Dual attention-enhanced data augmentation for diagnosing water management faults in proton exchange membrane fuel cells using imbalanced multi-sine AC data

Year: 2025

Type: article

Abstract: Proton exchange membrane fuel cell (PEMFC) faults, especially dehydration and flooding, cause distinct changes in electrochemical behavior. Consequently, real-time monitoring is essential for early and accurate diagnosis. However, acquiring real-world fault data is challenging, and the rarity of such faults results in severe class imbalance. This imbalance limits the performance, reliability, and practical applicability of conventional diagnostic methods. To address these limitations, this study proposes a unified diagnostic framework that integrates multi-sine AC voltage response, boundary-aware resampling, and attention-guided generative modeling. The key innovations of the proposed approach include: (1) Enhanced fault separability through the first application of multi-sine AC voltage response under data imbalance, enabling real-time extraction of critical electrochemical spectral features for early-stage diagnosis; (2) Improved data balance and clearer class boundaries using synthetic minority oversampling with Tomek links, which oversamples minority classes and removes borderline samples; (3) Realistic minority class synthesis using a dual attention Wasserstein generative adversarial networks, where channel attention focuses on diagnostically relevant spectral features and temporal attention models the dynamic evolution of PEMFC electrochemical behavior, ensuring high-quality, diagnostically informative synthetic fault data. The integrated framework achieves 99.67 % overall diagnostic accuracy and, under an extreme 1:200 class imbalance, outperforms state-of-the-art methods by 14 %. This approach enables rapid, data-efficient PEMFC fault diagnosis, strengthening fault management and advancing the performance of energy systems.

Source: Energy and AI

DOI: 10.1016/j.egyai.2025.100630

Authors: Abdullah Shoukat, Zhongyong Liu, Yousif Yahia Ahmed Abuker, Jingguo Li, Lei Mao

Institutions: University of Science and Technology of China, Tongling University

Language: English

Topic: Fuel Cells and Related Materials

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,630