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Degradations analysis and aging modeling for health assessment and prognostics of PEMFC.

, , , , and . Rel. Eng. & Sys. Safety, (2016)

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PEM fuel cell prognostics under variable load: A data-driven ensemble with new incremental learning., , , and . CoDIT, page 252-257. IEEE, (2016)ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network., , , , and . Mathematics and Computers in Simulation, (2017)Machine health condition prediction via online dynamic fuzzy neural networks., , , , and . Eng. Appl. of AI, (2014)Novel failure prognostics approach with dynamic thresholds for machine degradation., , and . IECON, page 4404-4409. IEEE, (2013)Defining and applying prediction performance metrics on a recurrent NARX time series model., , and . Neurocomputing, 73 (13-15): 2506-2521 (2010)Connexionist-Systems-Based Long Term Prediction Approaches for Prognostics., and . IEEE Trans. Reliability, 61 (4): 909-920 (2012)Degradations analysis and aging modeling for health assessment and prognostics of PEMFC., , , , and . Rel. Eng. & Sys. Safety, (2016)Improving accuracy of long-term prognostics of PEMFC stack to estimate remaining useful life., , , and . ICIT, page 1047-1052. IEEE, (2015)Fuel Cells prognostics using echo state network., , , , and . IECON, page 1632-1637. IEEE, (2013)Reducing arbitrary choices in model building for prognostics: An approach by applying parsimony principle on an evolving neuro-fuzzy system., , and . Microelectronics Reliability, 51 (2): 310-320 (2011)