针对质子交换膜燃料电池系统故障诊断问题,提出基于P-L双重特征提取的故障诊断方法。使用P-L双重特征提取对预处理后的样本数据进行特征提取,通过冗余变量剔除与二次特征提取,最大程度保留分类特征并有效降低样本数据维度。利用二叉树多类支持向量机与极限学习机对二维故障特征向量进行分类实现故障诊断。通过实例验证,对比线性判别分析的特征提取效果,P-L双重特征提取可使相同分类器测试集诊断准确率提高21.19%,诊断准确率达99.27%,实现了PEMFC系统膜干、氢气供应故障的精准快速诊断。
Abstract
For the fault diagnosis of proton exchange membrane fuel cell （PEMFC） system, a fault diagnosis method based on P-L dual feature extraction was proposed. P-L dual feature extraction is used to extract features from the preprocessed sample data. Through redundant variable removal and secondary feature extraction, classification features are preserved to the maximum extent and the dimension of sample data is effectively reduced. Binary tree multi-class support vector machine and extreme learning machine are used to classify 2D fault feature vectors and realize fault diagnosis. Through the example verification, compared with the feature extraction effect of linear discriminant analysis, P-L dual feature extraction improves the diagnostic accuracy of the test set of the same classifier by 21.19%, and the diagnostic accuracy reaches 99.27%, realizing the accurate and rapid diagnosis of membrane dry and hydrogen supply faults in PEMFC system.
关键词
质子交换膜燃料电池 /
故障检测 /
数据挖掘 /
P-L双重特征提取 /
支持向量机 /
极限学习机
Key words
proton exchange membrane fuel cell（PEMFC） /
fault detection /
data mining /
P-L dual feature extraction /
support vector machine（SVM） /
extreme learning machine（ELM）
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参考文献
[1] 周伟, 何华东. 浅谈质子交换膜燃料电池耐久性的研究现状[J]. 科技信息, 2010(26): 522-523.
ZHOU W, HE H D.Research status of durability of proton exchange membrane fuel cells[J]. Science & technology information, 2010(26): 522-523.
[2] 张雪霞, 蒋宇, 黄平, 等. 质子交换膜燃料电池容错控制方法综述[J]. 中国电机工程学报, 2021, 41(4): 1431-1444, 1549.
ZHANG X X, JIANG Y, HUANG P, et al.A review of fault-tolerant control methodology on proton exchange membrane fuel cell[J]. Proceedings of the CSEE, 2021, 41(4): 1431-1444, 1549.
[3] ZHANG X X, ZHOU J Z, CHEN W R.Data-driven fault diagnosis for PEMFC systems of hybrid tram based on deep learning[J]. International journal of hydrogen energy, 2020, 45(24): 13483-13495.
[4] 张雪霞, 高雨璇, 陈维荣. 基于数据驱动的质子交换膜燃料电池寿命预测[J]. 西南交通大学学报, 2020, 55(2): 417-427.
ZHANG X X, GAO Y X, CHEN W R.Data-driven based remaining useful life prediction for proton exchange membrane fuel cells[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 417-427.
[5] ZHANG X X, YU Z X, CHEN W R.Life prediction based on D-S ELM for PEMFC[J]. Energies, 2019, 12(19): 3752.
[6] ALLAM A, MANGOLD M, ZHANG P.Fault diagnosis of proton exchange membrane fuel cells[C]//2021 5th International Conference on Control and Fault-Tolerant Systems (SysTol). Saint-Raphael, France, 2021: 366-371.
[7] LI Z, GIURGEA S, OUTBIB R, et al.Online diagnosis of PEMFC by combining support vector machine and fluidic model[J]. Fuel cells, 2014, 14(3): 448-456.
[8] LI Z L, OUTBIB R, HISSEL D, et al.Online diagnosis of PEMFC by analyzing individual cell voltages[C]//2013 European Control Conference(ECC). Zurich, Switzerland, 2013: 2439-2444.
[9] LI Z L, OUTBIB R, GIURGEA S, et al.Fault detection and isolation for polymer electrolyte membrane fuel cell systems by analyzing cell voltage generated space[J]. Applied energy, 2015, 148: 260-272.
[10] 刘嘉蔚, 李奇, 陈维荣, 等. 基于概率神经网络和线性判别分析的PEMFC水管理故障诊断方法研究[J]. 中国电机工程学报, 2019, 39(12): 3614-3622.
LIU J W, LI Q, CHEN W R, et al.Research on PEMFC water management fault diagnosis method based on probabilistic neural network and linear discriminant analysis[J]. Proceedings of the CSEE, 2019, 39(12): 3614-3622.
[11] MAO L, JACKSON L, DUNNETT S.Fault diagnosis of practical polymer electrolyte membrane (PEM) fuel cell system with data-driven approaches[J]. Fuel cells, 2017, 17(2): 247-258.
[12] 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.
[13] 梁栋, 侯明, 窦美玲, 等. 质子交换膜燃料电池燃料饥饿现象[J]. 电源技术, 2010, 34(8): 767-770.
LIANG D, HOU M, DOU M L, et al.Study on behavior of proton exchange membrane fuel cell under fuel starvation conditions[J]. Chinese journal of power sources, 2010, 34(8): 767-770.
[14] 刘嘉蔚, 李奇, 陈维荣, 等. 基于在线序列超限学习机和主成分分析的蒸汽冷却型燃料电池系统快速故障诊断方法[J]. 电工技术学报, 2019, 34(18): 3949-3960.
LIU J W, LI Q, CHEN W R, et al.Fast fault diagnosis method of evaporatively cooled fuel cell system based on online sequential extreme learning machine and principal component analysis[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3949-3960.
[15] HUANG G B, ZHOU H M, DING X J, et al.Extreme learning machine for regression and multiclass classification[J]. IEEE transactions on systems, man, and cybernetics Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society, 2012, 42(2): 513-529.
基金
四川省科技厅重点研发计划（22ZDYF3375）; 西南交通大学基础研究培育支持计划（2682022ZTPY024）; 成都国佳电气工程有限公司资助项目（NEEC-2022-B010）
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