Journal
JOURNAL OF POWER SOURCES
Volume 482, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2020.228894
Keywords
Fault diagnosis; Fisher discriminant analysis; K-means clustering; PEMFC; Signal-to-image conversion
Funding
- National Natural Science Foundation of China (NSFC) [51975549]
- Anhui Provincial Natural Science Foundation [1908085ME161]
- State Key Laboratory of Mechanical System and Vibration [MSV202017]
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This paper proposes a novel approach for fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) using two-dimension (2D) image data. By analyzing test data from different faulty states, optimal features are determined to distinguish various states and compared with 1D voltage signals for effectiveness.
This paper proposes a novel approach for fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) with two-dimension (2D) image data, and investigates its effectiveness of identifying faults at different PEMFCs in terms of discrimination capacity and robustness. In the analysis, one-dimension (1D) voltage data from single cell is converted to corresponding 2D image using signal-to-image conversion technique. Various features are then extracted from the 2D image data, and optimal features are determined using Fisher discriminant analysis (FDA). Test data from PEMFC at different faulty states, including flooding and dehydration states, is collected for the analysis, and the effectiveness of optimal features in discriminating various states is investigated using K-means clustering method. Moreover, diagnostic performance with 2D image data is compared to those using 1D voltage signals, such that its effectiveness can be better highlighted. Furthermore, with test data collected from two different single cells, robustness of proposed method can be illustrated.
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