Data-driven Prognostics of Proton Exchange Membrane Fuel Cell Stack with constraint based Summation-Wavelet Extreme Learning Machine.
作者： Rafael Gouriveau , Noureddine Zerhouni , Daniel Hissel , Kamran Javed
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摘要: Aging of a fuel cell (FC) is an unavoidable process, nevertheless managing operating conditions and performing timely maintenance or control can prolong its life span. More precisely, the prognostics FC major area focus nowadays. This paper presents data-driven approach for Proton Exchange Membrane Fuel Cell (PEMFC) stack using constraint based Summation-Wavelet Extreme Learning Machine (SW-ELM). The proposition aims at improving robustness applicability aging PEMFC estimating RUL with limited data. proposed method applied to run-to-failure data from PHM challenge 2014, which had span 1155 hours. Performances are judged encounter parsimony problems. Results show adaptability SW-ELM learning suitability frequent intervals.
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