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• W4285014781 abstract "Remaining useful life (RUL) estimation is a key task in prognostics and health management. Due to the complexity of engineering systems, data-driven methods for the RUL estimation have been widely applied and developed through machine learning and artificial intelligence techniques. To enhance performance of these methods, improving data quality is as much important as developing sophisticated algorithms. This paper proposes learning of physical health timestep (PHT) using the long short-term memory (LSTM) network to replace the labeled timestep (LT) of a test unit. While the LT mainly records the timestep as an operation or observation index of the unit, the PHT estimates the unit's physical health from available sensory measurements. With the PHT, RUL estimation can be more accurate considering the unit's loading history. Effectiveness of the proposed methodology has been verified through experiments on lithium-ion battery and C-MAPSS engine datasets." @default.
• W4285014781 created "2022-07-12" @default.
• W4285014781 creator A5005330065 @default.
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• W4285014781 date "2022-10-01" @default.
• W4285014781 modified "2023-10-01" @default.
• W4285014781 title "Learning of physical health timestep using the LSTM network for remaining useful life estimation" @default.
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• W4285014781 doi "https://doi.org/10.1016/j.ress.2022.108717" @default.
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