A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm

Hanbin Dang; Rui Ma; Dongdong Zhao; Renyou Xie; Haiyan Li; Yuntian Liu

doi:10.1109/IECON43393.2020.9255167

A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm

Hanbin Dang
, Rui Ma
, Dongdong Zhao
, Renyou Xie
, Haiyan Li
, Yuntian Liu

School of Automation

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

As a renewable and efficient power source, proton exchange membrane fuel cells (PEMFCs) are receiving more and more attention from the world, but it still has shortcomings with poor durability and insufficient reliability, so the purpose of this paper is to effectively improve the reliability and durability of PEMFCs by a data-driven fault diagnosis method. In this method, principal component analysis (PCA) is first adopted to reduce the dimensionality of fault data. Then, a classification method named eXtreme Gradient Boosting (XGBoost) which based on boosting algorithm is used to classify these data. In the end, the experiment is proposed to verify the diagnostic performance of this method. The result shows that the method can effectively identify four healthy states of membrane drying failure, hydrogen leakage failure, normal state as well as unknown state, the diagnostic accuracy can reach 99.72%, and the diagnosis period is 0.17751 s, which is suitable for online implementation.

Original language	English
Title of host publication	Proceedings - IECON 2020
Subtitle of host publication	46th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
Pages	3951-3956
Number of pages	6
ISBN (Electronic)	9781728154145
DOIs	https://doi.org/10.1109/IECON43393.2020.9255167
State	Published - 18 Oct 2020
Event	46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore Duration: 19 Oct 2020 → 21 Oct 2020

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2020-October

Conference

Conference	46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Country/Territory	Singapore
City	Virtual, Singapore
Period	19/10/20 → 21/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

data-driven
online diagnosis
principle component analysis
proton exchange membrane fuel cells
XGBoost

Access to Document

10.1109/IECON43393.2020.9255167

Cite this

Dang, H., Ma, R., Zhao, D., Xie, R., Li, H., & Liu, Y. (2020). A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm. In Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society (pp. 3951-3956). Article 9255167 (IECON Proceedings (Industrial Electronics Conference); Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/IECON43393.2020.9255167

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title = "A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm",

abstract = "As a renewable and efficient power source, proton exchange membrane fuel cells (PEMFCs) are receiving more and more attention from the world, but it still has shortcomings with poor durability and insufficient reliability, so the purpose of this paper is to effectively improve the reliability and durability of PEMFCs by a data-driven fault diagnosis method. In this method, principal component analysis (PCA) is first adopted to reduce the dimensionality of fault data. Then, a classification method named eXtreme Gradient Boosting (XGBoost) which based on boosting algorithm is used to classify these data. In the end, the experiment is proposed to verify the diagnostic performance of this method. The result shows that the method can effectively identify four healthy states of membrane drying failure, hydrogen leakage failure, normal state as well as unknown state, the diagnostic accuracy can reach 99.72\%, and the diagnosis period is 0.17751 s, which is suitable for online implementation.",

keywords = "data-driven, online diagnosis, principle component analysis, proton exchange membrane fuel cells, XGBoost",

author = "Hanbin Dang and Rui Ma and Dongdong Zhao and Renyou Xie and Haiyan Li and Yuntian Liu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 ; Conference date: 19-10-2020 Through 21-10-2020",

year = "2020",

month = oct,

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doi = "10.1109/IECON43393.2020.9255167",

language = "英语",

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Dang, H, Ma, R, Zhao, D, Xie, R, Li, H & Liu, Y 2020, A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm. in Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society., 9255167, IECON Proceedings (Industrial Electronics Conference), vol. 2020-October, IEEE Computer Society, pp. 3951-3956, 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020, Virtual, Singapore, Singapore, 19/10/20. https://doi.org/10.1109/IECON43393.2020.9255167

A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm. / Dang, Hanbin; Ma, Rui; Zhao, Dongdong et al.
Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2020. p. 3951-3956 9255167 (IECON Proceedings (Industrial Electronics Conference); Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm

AU - Dang, Hanbin

AU - Ma, Rui

AU - Zhao, Dongdong

AU - Xie, Renyou

AU - Li, Haiyan

AU - Liu, Yuntian

PY - 2020/10/18

Y1 - 2020/10/18

N2 - As a renewable and efficient power source, proton exchange membrane fuel cells (PEMFCs) are receiving more and more attention from the world, but it still has shortcomings with poor durability and insufficient reliability, so the purpose of this paper is to effectively improve the reliability and durability of PEMFCs by a data-driven fault diagnosis method. In this method, principal component analysis (PCA) is first adopted to reduce the dimensionality of fault data. Then, a classification method named eXtreme Gradient Boosting (XGBoost) which based on boosting algorithm is used to classify these data. In the end, the experiment is proposed to verify the diagnostic performance of this method. The result shows that the method can effectively identify four healthy states of membrane drying failure, hydrogen leakage failure, normal state as well as unknown state, the diagnostic accuracy can reach 99.72%, and the diagnosis period is 0.17751 s, which is suitable for online implementation.

AB - As a renewable and efficient power source, proton exchange membrane fuel cells (PEMFCs) are receiving more and more attention from the world, but it still has shortcomings with poor durability and insufficient reliability, so the purpose of this paper is to effectively improve the reliability and durability of PEMFCs by a data-driven fault diagnosis method. In this method, principal component analysis (PCA) is first adopted to reduce the dimensionality of fault data. Then, a classification method named eXtreme Gradient Boosting (XGBoost) which based on boosting algorithm is used to classify these data. In the end, the experiment is proposed to verify the diagnostic performance of this method. The result shows that the method can effectively identify four healthy states of membrane drying failure, hydrogen leakage failure, normal state as well as unknown state, the diagnostic accuracy can reach 99.72%, and the diagnosis period is 0.17751 s, which is suitable for online implementation.

KW - data-driven

KW - online diagnosis

KW - principle component analysis

KW - proton exchange membrane fuel cells

KW - XGBoost

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T3 - IECON Proceedings (Industrial Electronics Conference)

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EP - 3956

BT - Proceedings - IECON 2020

PB - IEEE Computer Society

T2 - 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020

Y2 - 19 October 2020 through 21 October 2020

ER -

Dang H, Ma R, Zhao D, Xie R, Li H, Liu Y. A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm. In Proceedings - IECON 2020: 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society. 2020. p. 3951-3956. 9255167. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON43393.2020.9255167

A Novel Diagnosis Method of Proton Exchange Membrane Fuel Cells Based on the PCA and XGBoost Algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

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