Shared Temporal Attention Transformer for Remaining Useful Lifetime Estimation

Shared Temporal Attention Transformer for Remaining Useful Lifetime Estimation

This paper proposes a novel deep learning architecture for estimating the remaining useful lifetime (RUL) of industrial components, which solely relies on the recently developed transformer architectures. The RUL estimation resorts to analysing degradation patterns within multivariate time series si...

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Bibliographic Details
Main Authors: Gavneet Singh Chadha, Sayed Rafay Bin Shah, Andreas Schwung, Steven X. Ding
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Attention
neural networks
remaining useful lifetime estimation
time series analysis
transformer architecture
Online Access:https://ieeexplore.ieee.org/document/9812589/

Internet

https://ieeexplore.ieee.org/document/9812589/

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