Convolution-Graph Attention Network With Sensor Embeddings for Remaining Useful Life Prediction of Turbofan Engines
Author:
Affiliation:
1. School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, China
Funder
Central Guidance on Local Science and Technology Development Fund of Shenzhen
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10183770/10146287.pdf?arnumber=10146287
Reference25 articles.
1. A framework for predicting the remaining useful life of machinery working under time-varying operational conditions
2. Attention-based LSTM for Remaining Useful Life Estimation of Aircraft Engines
3. A multimodal and hybrid deep neural network model for Remaining Useful Life estimation
4. Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach
5. Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network
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