Fault Diagnosis of Practical Polymer Electrolyte Membrane (PEM) Fuel Cell System with Data‐driven Approaches1. Issue 2 (24th November 2016)

• Record Type: Journal Article
• Authors: Mao, L.; Jackson, L.; Dunnett, S.
• Other Names: Turpin Christophe guestEditor.; Yousfi‐Steiner Nadia guestEditor.; Bultel Yann guestEditor.; Hissel Daniel guestEditor.
• Is Part Of: Fuel cells. Volume 17:Issue 2(2017:Apr.)
• Page Start: 247
• Page End: 258
• Publication Date: 2016-11-24
• Subjects: Data‐driven Approach -- Fault Diagnosis -- Feature Selection -- PEM Fuel Cell -- Signal Processing Technique; Fuel cells -- Periodicals; 621.312429
• DOI: 10.1002/fuce.201600139
• Languages: English
• ISSNs: 1615-6846

Abstract

Abstract: In this paper, data‐driven approaches are applied to identify faults of a practical polymer electrolyte membrane (PEM) fuel cell system. Signal processing approaches are selected and employed to multiple sensor measurements, including methodologies reducing the dimension of the original dataset, and techniques extracting features. Both supervised and unsupervised techniques are applied in this study to investigate the robustness of the diagnostic procedure. Moreover, due to the fact that a series of features can be extracted from these sensors, the singular value decomposition (SVD) technique is applied to select features providing better diagnostic performance. Results demonstrate that with features selected from SVD, fuel cell system faults can be detected more effectively, and various fuel cell faults can also be discriminated with good quality. From the findings, conclusions are made and further work is suggested.