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Detection and Localization of a Gear Fault Using Automatic Continuous Monitoring of the Modulation Functions Read chapter Read first chapter

2021 | OriginalPaper | Chapter

A Novel Method of PEM Fuel Cell Fault Diagnosis Based on Signal-to-Image Conversion

Authors : Zhongyong Liu, Weitao Pan, Yousif Yahia Ahmed Abuker, Lei Mao

Published in: Advances in Condition Monitoring and Structural Health Monitoring

Publisher: Springer Singapore

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Abstract

This paper proposes an image processing-based approach for the fault diagnosis of polymer electrolyte membrane (PEM) fuel cells. As more abundant information is contained in the image than that that in 1D signal, features representing PEM fuel cell faults could be better highlighted with the image. Experimental data from a PEM fuel cell system at different states, including flooding and dehydration scenarios, is used to validate the proposed method. By converting the PEM fuel cell voltage signal into a 2D grey image, several features are extracted from the image, their performance in discriminating different PEM fuel cell states is investigated, and two optimal features are determined for fault diagnosis. Moreover, the diagnostic performance of optimal features from grey image is compared with features from PEM fuel cell voltage. Results demonstrate that better diagnostic performance could be obtained with the proposed method.

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previous chapter An Efficient Multi-scale-Based Multi-fractal Analysis Method to Extract Weak Signals for Gearbox Fault Diagnosis
next chapter Fault Classification of Polymer Electrolyte Membrane Fuel Cell System Based on Empirical Mode Decomposition
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Metadata
Title
A Novel Method of PEM Fuel Cell Fault Diagnosis Based on Signal-to-Image Conversion
Authors
Zhongyong Liu
Weitao Pan
Yousif Yahia Ahmed Abuker
Lei Mao
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Condition Monitoring and Structural Health Monitoring

Print ISBN: 978-981-15-9198-3

Electronic ISBN: 978-981-15-9199-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-9199-0_23

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