Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery
Author:
Funder
National Key R&D Program
National Natural Science Foundation of China
Publisher
Elsevier BV
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications
Reference38 articles.
1. Machinery health prognostics: a systematic review from data acquisition to rul prediction;Lei;Mech. Syst. Signal Process.,2018
2. Ensemble of optimized echo state networks for remaining useful life prediction;Rigamonti;Neurocomputing,2018
3. Finite-horizon fault estimation under imperfect measurements and stochastic communication protocol: dealing with finite-time boundedness;Dong;Int. J. Robust Nonlinear Control,2019
4. Set-membership filtering for state-saturated systems with mixed time-delays under weighted try-once-discard protocol;Li;IEEE Trans. Circuits Syst. II: Express Briefs,2019
5. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings;Wang;IEEE Trans. Reliab.,2018
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