Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis-Reference-Cited by-同舟云学术

Machine learning based online fault prognostics for nonstationary industrial process via degradation feature extraction and temporal smoothness analysis

Published:2021-12 Issue:12 Volume:28 Page:3838-3855
ISSN:2095-2899
Container-title:Journal of Central South University
language:en
Short-container-title:J. Cent. South Univ.

Author:

Hu Yun-yun,Zhao Chun-hui^ORCID,Ke Zhi-wu^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Metals and Alloys,General Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11771-021-4848-x.pdf

Reference34 articles.

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3. CHAI Zheng, ZHAO Chun-hui. Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification [J]. IEEE Transactions on Industrial Informatics, 2020, 16(1): 54–66. DOI: https://doi.org/10.1109/TII.2019.2915559.

4. DAI Yi, CHENG Shu, GAN Qin-jie, YU Tian-jian, WU Xun, BI Fu-liang. Life prediction of Ni-Cd battery based on linear Wiener process [J]. Journal of Central South University, 2021, 28(9): 2919–2930. DOI: https://doi.org/10.1007/s11771-021-4816-5.

5. ZHAO Chun-hui, GAO Fu-rong. Online fault prognosis with relative deviation analysis and vector autoregressive modeling [J]. Chemical Engineering Science, 2015, 138: 531–543. DOI: https://doi.org/10.1016/j.ces.2015.08.037.

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