A Hybrid Data Preprocessing-Based Hierarchical Attention BiLSTM Network for Remaining Useful Life Prediction of Spacecraft Lithium-Ion Batteries
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Luo, Tianyi, Liu, Ming 
ORCID: https://orcid.org/0000-0002-1846-6445, Shi, Peng ORCID: https://orcid.org/0000-0001-8218-586X, Duan, Guangren and Cao, Xibin ORCID: https://orcid.org/0000-0002-6923-0395
(2023)
A Hybrid Data Preprocessing-Based Hierarchical Attention BiLSTM Network for Remaining Useful Life Prediction of Spacecraft Lithium-Ion Batteries.
IEEE Transactions on Neural Networks and Learning Systems.
ISSN 2162-237X
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| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/47562 |
| DOI | 10.1109/TNNLS.2023.3311443 |
| Official URL | https://ieeexplore.ieee.org/document/10255373 |
| Subjects | Current > FOR (2020) Classification > 4008 Electrical engineering Current > Division/Research > Institute for Sustainable Industries and Liveable Cities |
| Keywords | remaining useful life prediction; RUL prediction; multiscale hierarchical attention bi-directional long short-term memory model; MHA-BiLSTM model |
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