基于MFDFA的PEMFC水淹和膜干故障诊断

刘川毓; 张雪霞; 蒋宇; 裴文慧; 陈维荣

doi:10.19912/j.0254-0096.tynxb.2022-0635

PDF(1867 KB)

太阳能学报 ›› 2023, Vol. 44 ›› Issue (8) : 85-91. DOI: 10.19912/j.0254-0096.tynxb.2022-0635

基于MFDFA的PEMFC水淹和膜干故障诊断

刘川毓, 张雪霞, 蒋宇, 裴文慧, 陈维荣

作者信息 +

FAULT DIAGNOSIS OF PEMFC FLOODING AND MEMBRANE DRYING BASED ON MFDFA

Liu Chuanyu, Zhang Xuexia, Jiang Yu, Pei Wenhui, Chen Weirong

Author information +

文章历史 +

摘要

为延长质子交换膜燃料电池（PEMFC）的使用寿命并提升其可靠性,采用基于信号处理的多重分形去趋势波动分析（MFDFA）方法对PEMFC的水管理故障进行诊断。基于MFDFA方法分别对正常、水淹和膜干3种状态下的电压信号进行分析。结果表明：故障时Gr(t)波动函数对数图相对分散、标度指数α在0~2之间、分形维数约为1.6与1.9,正常状态下Gr(t)波动函数对数图收敛、标度指数α在20~40之间、分形维数约为2.15。该方法通过以上3种指标结合,实现了PEMFC水淹和膜干故障诊断。

Abstract

In order to prolong the service life of proton exchange membrane fuel cell （PEMFC） and improve its reliability, a multifractal detrended fluctuation analysis （MFDFA） method based on signal analysis is used to diagnose the water-management issues within the PEMFC. The voltage signals under three states of normal, flooding and membrane drying are analyzed based on the MFDFA method, respectively. The results show that under the faulty states, the logarithmic graph of the Gr(t) wave function is relatively dispersive, the scaling exponent α is between 0 and 2, and the fractal dimension is about 1.6 and 1.9. Under normal state, the logarithm of the Gr(t) wave function is convergent, the scale exponent α is between 20 and 40, and the fractal dimension is about 2.15. This method accomplishes fault diagnosis of flooding and membrane drying inside PEMFC combining with the above indexes.

导出引用

刘川毓, 张雪霞, 蒋宇, 裴文慧, 陈维荣. 基于MFDFA的PEMFC水淹和膜干故障诊断[J]. 太阳能学报. 2023, 44(8): 85-91 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0635

Liu Chuanyu, Zhang Xuexia, Jiang Yu, Pei Wenhui, Chen Weirong. FAULT DIAGNOSIS OF PEMFC FLOODING AND MEMBRANE DRYING BASED ON MFDFA[J]. Acta Energiae Solaris Sinica. 2023, 44(8): 85-91 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0635

中图分类号： TM911

参考文献

[1] BAYKARA S Z.Hydrogen: a brief overview on its sources, production and environmental impact[J]. International journal of hydrogen energy,2018,43(23):10605-10614.
[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] CHUNG S, SHIN D, CHOUN M, et al.Improved water management of Pt/C cathode modified by graphitized carbon nanofiber in proton exchange membrane fuel cell[J]. Journal of power sources, 2018, 399: 350-356.
[5] 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.
[6] ZHANG X X, GUO X Q.Fault diagnosis of proton exchange membrane fuel cell system of tram based on information fusion and deep learning[J]. International journal of hydrogen energy, 2021, 46(60): 30828-30840.
[7] ZHENG Z X, PÉRA M C, HISSEL D, et al. A double-fuzzy diagnostic methodology dedicated to online fault diagnosis of proton exchange membrane fuel cell stacks[J]. Journal of power sources, 2014, 271: 570-581.
[8] ZUO B, ZHANG Z H, CHENG J S, et al.Data-driven flooding fault diagnosis method for proton-exchange membrane fuel cells using deep learning technologies[J]. Energy conversion and management, 2022, 251: 115004.
[9] ZHENG Z, PETRONE R, PÉRA M C, et al. A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems[J]. International journal of hydrogen energy, 2013, 38(21): 8914-8926.
[10] BENOUIOUA D, CANDUSSO D, HAREL F, et al.Fuel cell diagnosis method based on multifractal analysis of stack voltage signal[J]. International journal of hydrogen energy, 2014, 39(5): 2236-2245.
[11] BENOUIOUA D, CANDUSSO D, HAREL F, et al.Multifractal analysis of stack voltage based on wavelet leaders: a new tool for PEMFC diagnosis[J]. Fuel cells, 2017, 17(2): 217-224.
[12] KANTELHARDT J W, ZSCHIEGNER S A, KOSCIELNY-BUNDE E, et al.Multifractal detrended fluctuation analysis of nonstationary time series[J]. Physica A: statistical mechanics and its applications, 2002, 316(1-4): 87-114.
[13] MANDELBROT B B.The fractal geometry of nature[M]. San Francisco: W.H. Freeman, 1982.
[14] MAO L, JACKSON L, HUANG W G, et al.Polymer electrolyte membrane fuel cell fault diagnosis and sensor abnormality identification using sensor selection method[J]. Journal of power sources, 2020, 447: 227394.
[15] MAO L, JACKSON L.Effect of sensor set size on polymer electrolyte membrane fuel cell fault diagnosis[J]. Sensors, 2018, 18(9): 2777.
[16] MAO L, DAVIES B, JACKSON L.Application of the sensor selection approach in polymer electrolyte membrane fuel cell prognostics and health management[J]. Energies, 2017, 10(10): 1511.
[17] PENG C K, HAVLIN S, STANLEY H E, et al.Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series[J]. Chaos, 1995, 5(1): 82-87.
[18] MANDELBROT B B.Possible refinement of the lognormal hypothesis concerning the distribution of energy dissipation in intermittent turbulence[M]. Springer Berlin Heidelberg, 1972.
[19] MANDELBROT B B.A fractal set is one for which the fractal (Hausdorff-Besicovitch) dimension strictly exceeds the topological dimension[M]. Fractals and chaos. Springer, 2004.
[20] MANDAL S, SINHA N.Arrhythmia diagnosis from ECG signal analysis using statistical features and novel classification method[J]. Journal of mechanics in medicine and biology, 2021, 21(3): 2150025.
[21] ZHANG L L, LI H,LIU D, et al.Application of an improved multifractal detrended fluctuation analysis approach for estimation of the complexity of daily precipitation[J]. International journal of climatology: a journal of the royal meteorological society, 2021, 41(9): 4653-4671.
[22] 秦宁. 基于振动信号分析的滚动轴承故障诊断研究[D]. 大连: 大连理工大学, 2020.
QIN N.Research on fault diagnosis of rolling bearing based on vibration signal analysis[D]. Dalian: Dalian University of Technology, 2020.
[23] 奚彩萍. 随机多重分形信号奇异性谱分析及其相关应用[D]. 南京:南京理工大学, 2017.
XI C P.Research on the singularity spectrum analysis of the stochastic multifractal signal and the related applications[D]. Nanjing: Nanjing University of Science and Technology, 2017.
[24] 刘岩. 基于变分模态分解与奇异谱分析的往复压缩机典型故障预示研究[D]. 大庆: 东北石油大学, 2018.
LIU Y.Typical faults prognosis for reciprocating compressors based on variational mode decomposition and singular spectrum analysis[D]. Daqing: Northeast Petroleum University, 2018.
[25] 谢贤健, 韦方强. 泥石流频发区不同盖度草地土壤颗粒的分形特征[J]. 水土保持学报, 2011, 25(4): 202-206.
XIE X J, WEI F Q.Characteristics of soil particle fractal dimension under different coverage grassland of the area with high-frequency debris flow[J]. Journal of soil and water conservation, 2011, 25(4): 202-206.

基金

国家自然科学基金青年基金（51607149）; 2022年度西南交通大学基础研究培育支持计划项目学科交叉专项（2682022ZTPY024）; 2021年度成都西南交通大学国家轨道交通电气化与自动化工程技术研究中心开放课题基金（面上）计划（NEEC-2022-B010）; 四川省重点研发计划项目（22ZDYF3375）