A Data-Driven Model for Bearing Remaining Useful Life Prediction with Multi-step Long Short-Term Memory Network-Reference-Cited by-同舟云学术

A Data-Driven Model for Bearing Remaining Useful Life Prediction with Multi-step Long Short-Term Memory Network

Published:2022-07-02 Issue: Volume: Page:1129-1138
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Zhao Zhen,Du Zehou,Yang Kaixuan,Sun Haoqin,Wei Jialong,Liu Yang

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-3387-5_135

Reference19 articles.

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2. Liao, L.: Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans. Ind. Electron. 61(5), 2464–2472 (2013)

3. Wang, J., Gao, R.X., Yuan, Z., et al.: A joint particle filter and expectation maximization approach to machine condition prognosis. J. Intell. Manuf. 30, 1–17 (2017)

4. Kirubarajan, T.: Physically based diagnosis and prognosis of cracked rotor shafts. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 4733 (2002)

5. Chehade, A., Bonk, S., Liu, K.: Sensory-based failure threshold estimation for remaining useful life prediction. IEEE Trans. Rel. 66(3), 939–949 (2017)

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