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

Shi Yong; Huang Ning; Xie Di; Yao Jigang; Wang Liangliang

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

PDF(2730 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 53-61. DOI: 10.19912/j.0254-0096.tynxb.2024-0149

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 +

History +

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.

Key words

proton exchange membrane fuel cells （PEMFC） / fault diagnosis / convolutional neural networks / self-attention mechanism

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

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

References

[1] Energy Department Hydrogen Program Plan[EB/OL]. https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/hydrogen-program-plan-2020.pdf?Status=Master
[2] 陈维荣, 刘嘉蔚, 李奇, 等. 质子交换膜燃料电池故障诊断方法综述及展望[J]. 中国电机工程学报, 2017, 37(16): 4712-4721.
CHEN W R, LIU J W, LI Q, et al.Review and prospect of fault diagnosis methods for proton exchange membrane fuel cell[J]. Proceedings of the CSEE, 2017, 37(16): 4712-4721.
[3] ESCOBET T, FEROLDI D, DE LIRA S, et al.Model-based fault diagnosis in PEM fuel cell systems[J]. Journal of power sources, 2009, 192(1): 216-223.
[4] 周苏, 任宏伟, 裴冯来. 基于诊断用COMSOL模型的PEMFC故障诊断方法研究[J]. 化学工业与工程, 2015, 32(4): 56-62.
ZHOU S, REN H W, PEI F L.Faults diagnosis method study of PEMFC based on a diagnostic COMSOL model[J]. Chemical industry and engineering, 2015, 32(4): 56-62.
[5] 朱静, 赵静欣. 质子交换膜燃料电池系统数字孪生故障诊断模型研究[J]. 控制理论与应用, 2022, 39(3): 527-534.
ZHU J, ZHAO J X.Digital twin fault diagnosis model analysis of proton exchange membrane fuel cell systems[J]. Control theory & applications, 2022, 39(3): 527-534.
[6] DE LIRA S, PUIG V, QUEVEDO J.LPV model-based fault diagnosis using relative fault sensitivity signature approach in a PEM fuel cell[J]. IFAC proceedings volumes, 2009, 42(8): 528-533.
[7] 雍加望, 赵倩倩, 冯能莲. 基于非线性动态模型的质子交换膜燃料电池故障诊断[J]. 化工学报, 2022, 73(9): 3983-3993.
YONG J W, ZHAO Q Q, FENG N L.Fault diagnosis of proton exchange membrane fuel cell based on nonlinear dynamic model[J]. CIESC journal, 2022, 73(9): 3983-3993.
[8] ESMAILI Q, NIMVARI M E, JOUYBARI N F, et al.Model based water management diagnosis in polymer electrolyte membrane fuel cell[J]. International journal of hydrogen energy, 2020, 45(31): 15618-15629.
[9] 李晗, 萧德云. 基于数据驱动的故障诊断方法综述[J]. 控制与决策, 2011, 26(1): 1-9, 16.
LI H, XIAO D Y.Survey on data driven fault diagnosis methods[J]. Control and decision, 2011, 26(1): 1-9, 16.
[10] LI Z L, OUTBIB R, GIURGEA S, et al.Online implementation of SVM based fault diagnosis strategy for PEMFC systems[J]. Applied energy, 2016, 164: 284-293.
[11] XIAO F, CHEN T, ZHANG J W, et al.Water management fault diagnosis for proton-exchange membrane fuel cells based on deep learning methods[J]. International journal of hydrogen energy, 2023, 48(72): 28163-28173.
[12] LIU Z Y, PEI M L, HE Q B, et al.A novel method for polymer electrolyte membrane fuel cell fault diagnosis using 2D data[J]. Journal of power sources, 2021, 482: 228894.
[13] 刘昕宇, 韩莹, 陈维荣, 等. 基于改进SSA-DBN的质子交换膜燃料电池水故障智能分类方法[J]. 电力自动化设备, 2024, 44(4): 18-24.
LIU X Y, HAN Y, CHEN W R, et al.Intelligent classification method of water faults for proton exchange membrane fuel cell based on improved SSA-DBN[J]. Electric power automation equipment, 2024, 44(4): 18-24.
[14] 贺飞, 张雪霞, 陈维荣. 基于P-L双重特征提取的PEMFC系统故障诊断方法[J]. 太阳能学报, 2024, 45(1): 492-499.
HE F, ZHANG X X, CHEN W R.Fault diagnosis method of PEMFC system based on P-L dual feature extraction[J]. Acta energiae solaris sinica, 2024, 45(1): 492-499.
[15] DU R B, WEI X Z, WANG X Y, et al.A fault diagnosis model for proton exchange membrane fuel cell based on impedance identification with differential evolution algorithm[J]. International journal of hydrogen energy, 2021, 46(78): 38795-38808.
[16] 刘相万, 杨扬, 朱文超, 等. 基于二阶RQ-RLC模型的质子交换膜燃料电池水管理故障诊断[J]. 中国电机工程学报, 2022, 42(21): 7893-7905.
LIU X W, YANG Y, ZHU W C, et al.Second-order RQ-RLC model-based fault diagnosis for water management in proton exchange membrane fuel cells[J]. Proceedings of the CSEE, 2022, 42(21): 7893-7905.
[17] 万伟东. 基于EIS和数据驱动的PEMFC电堆健康状态监测方法研究[D]. 大连: 大连理工大学, 2022.
WAN W D.Research on health status monitoring method for PEMFC stack based on EIS and data-driven[D]. Dalian: Dalian University of Technology, 2022.
[18] 卢华歆. 基于快速电化学阻抗谱测量的在线质子交换膜燃料电池故障诊断[D]. 杭州: 浙江大学, 2020.
LU H X.On-line fault diagnosis for PEM fuel cells based on fast EIS measurement[D]. Hangzhou: Zhejiang University, 2020.
[19] ZHANG H C, LV J F, KUANG J Y, et al.Data-driven based PEMFC EIS modeling with nyquist plot[C]//IECON 2022-48th Annual Conference of the IEEE Industrial Electronics Society. Brussels, Belgium, 2022: 1-6.
[20] LYU J F, KUANG J Y, YU Z L, et al.Diagnosis of PEM fuel cell system based on electrochemical impedance spectroscopy and deep learning method[J]. IEEE transactions on industrial electronics, 2024, 71(1): 657-666.
[21] HAN S B, OH H, LEE W Y, et al.On-line EIS measurement for high-power fuel cell systems using Simulink real-time[J]. Energies, 2021, 14(19): 6133.
[22] 黄赵军, 苏建徽, 解宝, 等. 基于模糊C均值聚类和概率神经网络的PEMFC故障诊断方法研究[J]. 太阳能学报, 2024, 45(1): 475-483.
HUANG Z J, SU J H, XIE B, et al.Research on PEMFC fault diagnosis method based on fuzzy C means clustering and probabilistic neural network[J]. Acta energiae solaris sinica, 2024, 45(1): 475-483.
[23] 金致含, 苏建徽, 施永, 等. 基于二进制序列的燃料电池宽频带EIS测试[J]. 太阳能学报, 2022, 43(7): 1-8.
JIN Z H, SU J H, SHI Y, et al.Broadband EIS test of fuel cell based on binary sequences[J]. Acta energiae solaris sinica, 2022, 43(7): 1-8.
[24] 姚鹏. 人工智能技术在交流异步电机故障诊断中的应用[J]. 电机与控制应用, 2022, 49(4): 1-9.
YAO P.Application of artificial intelligence technology in fault diagnosis of AC asynchronous motor[J]. Electric machines & control application, 2022, 49(4): 1-9.
[25] 何雅静, 汪登. 人工神经网络在光伏中的应用综述[J]. 太阳能, 2019(1): 17-20.
HE Y J, WANG D.Review on the application of neural networks in PV[J]. Solar energy, 2019(1): 17-20.
[26] 林凡勤, 李明明, 郭红. 变压器故障诊断技术综述[J]. 计算机与现代化, 2022(3): 116-126.
LIN F Q, LI M M, GUO H.Review on fault diagnosis technology of transformer[J]. Computer and modernization, 2022(3): 116-126.
[27] ZHU L, CHEN J.Prognostics of PEM fuel cells based on Gaussian process state space models[J]. Energy, 2018, 149: 63-73.
[28] BENMOUNA A, BECHERIF M, DEPERNET D, et al.Fault diagnosis methods for proton exchange membrane fuel cell system[J]. International journal of hydrogen energy, 2017, 42(2): 1534-1543.
[29] 马睿, 党翰斌, 张钰奇, 等. 质子交换膜燃料电池系统故障机理分析及诊断方法研究综述[J]. 中国电机工程学报, 2024, 44(1): 407-426.
MA R, DANG H B, ZHANG Y Q, et al.A review on failure mechanism analysis and diagnosis for proton exchange membrane fuel cell systems[J]. Proceedings of the CSEE, 2024, 44(1): 407-426.
[30] 张雪霞, 蒋宇, 孙腾飞, 等. 质子交换膜燃料电池水淹和膜干故障诊断研究综述[J]. 西南交通大学学报, 2020, 55(4): 828-838, 864.
ZHANG X X, JIANG Y, SUN T F, et al.Review on fault diagnosis for flooding and drying in proton exchange membrane fuel cells[J]. Journal of Southwest Jiaotong University, 2020, 55(4): 828-838, 864.
[31] SHI Y, HE W, XIE B, et al.PEMFC fault diagnosis based on an equivalent circuit and OS-ELM framework[J]. IEEE transactions on industry applications, 2024, 60(1): 1277-1287.