Compact Convolutional Transformer for Bearing Remaining Useful Life Prediction
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-52649-7_18
Reference24 articles.
1. Chen C et al (2021) An integrated deep learning-based approach for automobile maintenance prediction with GIS Data. Reliab Eng Syst Saf 216:107919. https://doi.org/10.1016/j.ress.2021.107919
2. Chen D et al (2021) Health indicator construction by quadratic function-based deep convolutional auto-encoder and its application into bearing RUL prediction. ISA Trans 114:44–56. https://doi.org/10.1016/j.isatra.2020.12.052
3. Chen Y, et al (2020) A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Appl Soft Comput 86:105919. https://doi.org/10.1016/j.asoc.2019.105919.
4. Deutsch J, He M, He D (2017) Remaining useful life prediction of hybrid ceramic bearings using an integrated deep learning and particle filter approach. Appl Sci 7(7):649. https://doi.org/10.3390/app7070649
5. Deng F, Chen Z, Liu Y, Yang S, Hao R, Lyu L (2022) A Novel combination neural network based on ConvLSTM-transformer for bearing remaining useful life prediction. Machines 10:1226. https://doi.org/10.3390/machines10121226
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