A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems
Reliability Engineering & System Safety(2022)
Abstract
•A novel dual-stream data-driven framework is proposed for RUL estimation.•Multi-head self-attention mechanism is employed to enrich features with correlation information.•A data transformation algorithm is designed to obtain internal differences of data.•Experiments on the C-MAPSS datasets show the superiority of the proposed method.
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Key words
Prognostic and health management,Remaining useful life estimation,Deep learning,Self-attention neural network
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