Online implementation of SVM based fault diagnosis strategy for PEMFC systems

被引：80

作者：

Li, Zhongliang ^{[1
,2
]}

Outbib, Rachid ^{[3
]}

Giurgea, Stefan ^{[1
,2
]}

Hissel, Daniel ^{[1
,2
]}

Jemei, Samir ^{[1
,2
]}

Giraud, Alain ^{[4
]}

Rosini, Sebastien ^{[5
]}

机构：

[1] CNRS, FR 3539, FCLAB Fuel Cell Lab Res Federat, Rue Thierry Mieg, F-90010 Belfort, France

[2] UFC UTBM ENSMM, Dept Energy, CNRS, FEMTO ST,UMR 6174, Paris, France

[3] Univ Aix Marseille, LSIS, Marseille, France

[4] CEA, LIST, F-91191 Gif Sur Yvette, France

[5] CEA Grenoble, LITEN, F-38054 Grenoble, France

来源：

APPLIED ENERGY | 2016年 / 164卷

关键词：

PEMFC system; Fault diagnosis; SVM classification; System in Package; Online implementation; FUEL-CELL; MODEL; STACK; METHODOLOGIES;

D O I：

10.1016/j.apenergy.2015.11.060

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, the topic of online diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems is addressed. In the diagnosis approach, individual cell voltages are used as the variables for diagnosis. The pattern classification tool Support Vector Machine (SVM) combined with designed diagnosis rule is used to achieve fault detection and isolation (FDI). A highly-compacted embedded system of the System in Package (SiP) type is designed and fabricated to monitor individual cell voltages and to perform the diagnosis algorithms. For validation, the diagnosis approach is implemented online on PEMFC experimental platform. Four concerned faults can be detected and isolated in real-time. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：284 / 293

页数：10

共 50 条

[21] Bearing fault diagnosis based on PCA and SVM
Shuang, Lu
Meng, Li
[J]. 2007 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION, VOLS I-V, CONFERENCE PROCEEDINGS, 2007, : 3503 - +
[22] Data-driven fault diagnosis for PEMFC systems of hybrid tram based on deep learning
Zhang, Xuexia
Zhou, Jingzhe
Chen, Weirong
[J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2020, 45 (24) : 13483 - 13495
[23] An Online Model-based Fault Diagnosis Scheme for HVAC Systems
Thumati, Balaje T.
Feinstein, Miles A.
Fonda, James W.
Turnbull, Alfred
Weaver, Fay J.
Calkins, Mark E.
Jagannathan, S.
[J]. 2011 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS (CCA), 2011, : 70 - 75
[24] Adaptive online fault diagnosis of manufacturing systems based on DEVS formalism
Rajaoarisoa, L.
Sayed-Mouchaweh, M.
[J]. IFAC PAPERSONLINE, 2017, 50 (01): : 6825 - 6830
[25] Fault diagnosis method of PEMFC system based on ensemble learning
Zhang, Xuexia
Peng, Lishuo
He, Fei
Huang, Ruike
[J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2024, 69 : 1501 - 1510
[26] FAULT DIAGNOSIS OF PEMFC FLOODING AND MEMBRANE DRYING BASED ON MFDFA
Liu C.
Zhang X.
Jiang Y.
Pei W.
Chen W.
[J]. Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2023, 44 (08): : 85 - 91
[27] Fault Diagnosis of PEMFC Stack Based on PSO-DBN
Zhu, Shaopeng
Zhang, Bo
Wang, Liming
Chen, Ping
Chen, Huipeng
Xu, Yekai
[J]. PROCEEDINGS OF THE 10TH HYDROGEN TECHNOLOGY CONVENTION, VOL 3, WHTC 2023, 2024, 395 : 206 - 216
[28] Fault Diagnosis for PEMFC Systems in Consideration of Dynamic Behaviors and Spatial Inhomogeneity
Li, Zhongliang
Outbib, Rachid
Giurgea, Stefan
Hissel, Daniel
[J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 2019, 34 (01) : 3 - 11
[29] Fault Diagnosis of PEMFC Systems In The Model Space Using Reservoir Computing
Zheng, Zhixue
Pera, Marie-Cecile
Hissel, Daniel
Larger, Laurent
Steiner, Nadia Yousfi
Jemei, Samir
[J]. 2018 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC), 2018,
[30] Fault diagnosis of rotating shaft systems based on wavelet entropy and GA-SVM
Hu, Hai-Gang
Zhou, Xin
Feng, Zhi-Min
[J]. Journal of Applied Sciences, 2013, 13 (16) : 3209 - 3214

← 1 2 3 4 5 →