Unsupervised machinery prognostics approach based on wavelet packet decomposition and variational autoencoder
Author:
Funder
Fundação de Amparo à Pesquisa do Estado de São Paulo
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40430-023-04674-0.pdf
Reference41 articles.
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2. Niu H, Zeng J, Shi H, Zhang X, Liang J (2023) Degradation modeling and remaining useful life prediction for a multi-component system with stochastic dependence. Comput Ind Eng 175:108889. https://doi.org/10.1016/j.cie.2022.108889
3. Souza MLH, Costa CA, Oliveira Ramos G (2023) A machine-learning based data-oriented pipeline for prognosis and health management systems. Comput Ind 148:103903. https://doi.org/10.1016/j.compind.2023.103903
4. Carvalho TP, Soares FAAMN, Vita RP, Francisco R, Basto JP, Alcalá SGS (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 137:106024. https://doi.org/10.1016/j.cie.2019.106024
5. Nemani VP, Lu H, Thelen A, Hu C, Zimmerman AT (2022) Ensembles of probabilistic LSTM predictors and correctors for bearing prognostics using industrial standards. Neurocomputing 491:575–596. https://doi.org/10.1016/j.neucom.2021.12.035
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