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This paper presents a novel method for degradation modeling and remaining useful life (RUL) prediction of multi-component systems under triple uncertainties, including aleatory, epistemic, and measurement uncertainties. The proposed approach integrates deep learning techniques, specifically a deep belief network and self-organizing map neural network, to extract degradation features and construct a system-level health index. The effectiveness of the method is validated using the C-MAPSS dataset, demonstrati

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1179772391

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