Remaining Useful Life Prediction for Aero-Engines Using a Time-Enhanced Multi-Head Self-Attention Model

Remaining Useful Life Prediction for Aero-Engines Using a Time-Enhanced Multi-Head Self-Attention Model

Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on the attention mechanism. The attention model is...

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Bibliographic Details
Main Authors: Xin Wang, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng, Bo Zheng
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Aerospace
Subjects:
deep learning
RUL prediction
data-driven
temporal prediction
self-attention
Online Access:https://www.mdpi.com/2226-4310/10/1/80

Internet

https://www.mdpi.com/2226-4310/10/1/80

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