e-RULENet: remaining useful life estimation with end-to-end learning from long run-to-failure data
Author:
Affiliation:
1. NEC Corporation, Kawasaki, Japan
Publisher
Informa UK Limited
Subject
General Medicine
Link
https://www.tandfonline.com/doi/pdf/10.1080/18824889.2023.2195982
Reference19 articles.
1. Zheng S Ristovski K Farahat A et al. Long short-term memory network for remaining useful life estimation. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM); June 2017. p. 88–95.
2. Hybrid method for remaining useful life prediction in wind turbine systems
3. Li M Sadoughi M Shen S et al. Remaining useful life prediction of lithium-ion batteries using multi-model Gaussian process. In: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM); June 2019. p. 1–6.
4. Liu Y Chang Y Liu S et al. Data-driven prognostics of remaining useful life for milling machine cutting tools. In: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM); June 2019. p. 1–5.
5. A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques
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