Uncertainty Quantification and Interval Prediction of Equipment Remaining Useful Life Based on Semisupervised Learning
Author:
Affiliation:
1. State Key Laboratory of Computer-Aided Design and Computer Graphics (CAD&CG), Zhejiang University, Hangzhou, China
2. Zhejiang Laboratory, Hangzhou, China
Funder
National Natural Science Foundation of China
Key Research and Development Program of Zhejiang Province
High-Level Talent Special Support Plan of Zhejiang Province
Zhejiang Provincial Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10367905/10330604.pdf?arnumber=10330604
Reference39 articles.
1. Bearing Remaining Useful Life Prediction Based on Regression Shapalet and Graph Neural Network
2. Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction
3. Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism
4. Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture
5. Prediction Interval Estimation of Aeroengine Remaining Useful Life Based on Bidirectional Long Short-Term Memory Network
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1. MST-GAT: A multi-perspective spatial-temporal graph attention network for multi-sensor equipment remaining useful life prediction;Information Fusion;2024-10
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3. Parallel processing of sensor signals using deep learning method for aero-engine remaining useful life prediction;Measurement Science and Technology;2024-06-26
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