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PEM fuel cell prognostics under variable load: A data-driven ensemble with new incremental learning., , , и . CoDIT, стр. 252-257. IEEE, (2016)Parametric sensitivity analysis of a PEMFC physics-based model developed for prognostics., , , , и . ICPHM, стр. 1-7. IEEE, (2015)ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network., , , , и . Math. Comput. Simul., (2017)Machine health condition prediction via online dynamic fuzzy neural networks., , , , и . Eng. Appl. Artif. Intell., (2014)Novel failure prognostics approach with dynamic thresholds for machine degradation., , и . IECON, стр. 4404-4409. IEEE, (2013)Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics., , , и . IEEE Trans. Ind. Electron., 62 (1): 647-656 (2015)Risk management - dependability tools and case-based reasoning integration using the object formalism., и . Comput. Ind., 55 (3): 255-267 (2004)SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization., , и . Neurocomputing, (2014)Joint Particle Filters Prognostics for Proton Exchange Membrane Fuel Cell Power Prediction at Constant Current Solicitation., , , , и . IEEE Trans. Reliability, 65 (1): 336-349 (2016)Reducing arbitrary choices in model building for prognostics: An approach by applying parsimony principle on an evolving neuro-fuzzy system., , и . Microelectron. Reliab., 51 (2): 310-320 (2011)