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: 0000-0002-1846-6445, Shi, Peng ORCID: 0000-0001-8218-586X, Duan, Guangren and Cao, Xibin ORCID: 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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