Diagnosis of CO Pollution in HTPEM Fuel Cell using Statistical Change Detection

Christian Jeppesen; Mogens Blanke; Fan Zhou; Søren Juhl Andreasen

doi:10.1016/j.ifacol.2015.09.583

Diagnosis of CO Pollution in HTPEM Fuel Cell using Statistical Change Detection

Christian Jeppesen
, Mogens Blanke
, Fan Zhou
, Søren Juhl Andreasen

Aalborg University

Research output: Contribution to journal › Conference article › Research › peer-review

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Abstract

The fuel cell technologies are advancing and maturing for commercial markets. However proper diagnostic tools needs to be developed in order to insure reliability and durability of fuel cell systems. This paper presents a design of a data driven method to detect CO content in the anode gas of a high temperature fuel cell. In this work the fuel cell characterization is based on an experimental equivalent electrical circuit, where model parameters are mapped as a function of the load current. The designed general likelihood ratio test detection scheme detects whether a equivalent electrical circuit parameter differ from the non-faulty operation. It is proven that the general likelihood ratio test detection scheme, with a very low probability of false alarm, can detect CO content in the anode gas of the fuel cell.

Original language	English
Book series	IFAC-PapersOnLine
Volume	48
Issue number	22
Pages (from-to)	547-553
ISSN	2405-8963
DOIs	https://doi.org/10.1016/j.ifacol.2015.09.583
Publication status	Published - 2015
Event	9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes - Arts et Métiers ParisTech, Paris, France Duration: 2 Sept 2015 → 4 Sept 2015 Conference number: 9 https://www.sciencedirect.com/journal/ifac-papersonline/vol/48/issue/21

Conference

Conference	9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes
Number	9
Location	Arts et Métiers ParisTech
Country/Territory	France
City	Paris
Period	02/09/2015 → 04/09/2015
Internet address	https://www.sciencedirect.com/journal/ifac-papersonline/vol/48/issue/21

Bibliographical note

9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015. Edited by Didier Maquin.

Keywords

Change detection
GLRT
Fault Diagnosis
PEM Fuel Cell
HTPEM
EIS

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title = "Diagnosis of CO Pollution in HTPEM Fuel Cell using Statistical Change Detection",

abstract = "The fuel cell technologies are advancing and maturing for commercial markets. However proper diagnostic tools needs to be developed in order to insure reliability and durability of fuel cell systems. This paper presents a design of a data driven method to detect CO content in the anode gas of a high temperature fuel cell. In this work the fuel cell characterization is based on an experimental equivalent electrical circuit, where model parameters are mapped as a function of the load current. The designed general likelihood ratio test detection scheme detects whether a equivalent electrical circuit parameter differ from the non-faulty operation. It is proven that the general likelihood ratio test detection scheme, with a very low probability of false alarm, can detect CO content in the anode gas of the fuel cell.",

keywords = "Change detection, GLRT, Fault Diagnosis, PEM Fuel Cell, HTPEM, EIS",

author = "Christian Jeppesen and Mogens Blanke and Fan Zhou and Andreasen, \{S{\o}ren Juhl\}",

note = "9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015. Edited by Didier Maquin.; 9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes, SAFEPROCESS 2015 ; Conference date: 02-09-2015 Through 04-09-2015",

year = "2015",

doi = "10.1016/j.ifacol.2015.09.583",

language = "English",

volume = "48",

pages = "547--553",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "Elsevier",

number = "22",

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T1 - Diagnosis of CO Pollution in HTPEM Fuel Cell using Statistical Change Detection

AU - Jeppesen, Christian

AU - Blanke, Mogens

AU - Zhou, Fan

AU - Andreasen, Søren Juhl

N1 - Conference code: 9

PY - 2015

Y1 - 2015

N2 - The fuel cell technologies are advancing and maturing for commercial markets. However proper diagnostic tools needs to be developed in order to insure reliability and durability of fuel cell systems. This paper presents a design of a data driven method to detect CO content in the anode gas of a high temperature fuel cell. In this work the fuel cell characterization is based on an experimental equivalent electrical circuit, where model parameters are mapped as a function of the load current. The designed general likelihood ratio test detection scheme detects whether a equivalent electrical circuit parameter differ from the non-faulty operation. It is proven that the general likelihood ratio test detection scheme, with a very low probability of false alarm, can detect CO content in the anode gas of the fuel cell.

AB - The fuel cell technologies are advancing and maturing for commercial markets. However proper diagnostic tools needs to be developed in order to insure reliability and durability of fuel cell systems. This paper presents a design of a data driven method to detect CO content in the anode gas of a high temperature fuel cell. In this work the fuel cell characterization is based on an experimental equivalent electrical circuit, where model parameters are mapped as a function of the load current. The designed general likelihood ratio test detection scheme detects whether a equivalent electrical circuit parameter differ from the non-faulty operation. It is proven that the general likelihood ratio test detection scheme, with a very low probability of false alarm, can detect CO content in the anode gas of the fuel cell.

KW - Change detection

KW - GLRT

KW - Fault Diagnosis

KW - PEM Fuel Cell

KW - HTPEM

KW - EIS

U2 - 10.1016/j.ifacol.2015.09.583

DO - 10.1016/j.ifacol.2015.09.583

M3 - Conference article

SN - 2405-8963

VL - 48

SP - 547

EP - 553

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 22

T2 - 9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes

Y2 - 2 September 2015 through 4 September 2015

ER -

Diagnosis of CO Pollution in HTPEM Fuel Cell using Statistical Change Detection

Abstract

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Bibliographical note

Keywords

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