DocumentCode :
665988
Title :
Fuel Cells prognostics using echo state network
Author :
Morando, S. ; Jemei, S. ; Gouriveau, R. ; Zerhouni, N. ; Hissel, D.
Author_Institution :
Energy Dept., Univ. of Franche-Comte (UFC), Belfort, France
fYear :
2013
fDate :
10-13 Nov. 2013
Firstpage :
1632
Lastpage :
1637
Abstract :
One remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it is important to develop failure diagnostic and prognostic tools enabling the optimization of the FC. Among all the existing prognostics approaches, datamining methods such as artificial neural networks aim at estimating the process´ behavior without huge knowledge about the underlying physical phenomena. Nevertheless, this kind of approach needs huge learning dataset. Also, the deployment of such an approach can be long (trial and error method), which represents a real problem for industrial applications where real-time complying algorithms must be developed. According to this, the aim of this paper is to study the application of a reservoir computing tool (the Echo State Network) as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell. Developments emphasize on the prediction of the mean voltage cells of a degrading FC. Accuracy and time consumption of the approach are studied, as well as sensitivity of several parameters of the ESN. Results appear to be very promising.
Keywords :
condition monitoring; data mining; failure analysis; maintenance engineering; power engineering computing; proton exchange membrane fuel cells; recurrent neural nets; FC optimization; artificial neural networks; condition-based maintenance; data-mining methods; echo state network; failure diagnostic tool; failure prognostic tool; industria fuel cell prognostics; learning dataset; mean voltage cell prediction; process behavior estimation; proton exchange membrane fuel cell; real- time complying algorithms; remaining useful life estimation; system limited useful lifetime; technological bottleneck; trial-and-error method; Artificial intelligence; Equations; Fuel cells; Maintenance engineering; Mathematical model; Neurons; Reservoirs; Echo State Network; Multi-steps ahead Prediction; Prognostics; Proton Exchange Membrane Fuel Cell; Reservoir Computing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE
Conference_Location :
Vienna
ISSN :
1553-572X
Type :
conf
DOI :
10.1109/IECON.2013.6699377
Filename :
6699377
Link To Document :
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