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Newsletter 8호 국내외 최신 대표
논문
◆ N. Omri, Z. Al
Masry, N. Mairot, S. Giampiccolo, N. Zerhouni, Industrial data management
strategy towards an SME-oriented PHM, Journal of Manufacturing Systems, Volume
56, 2020
DOI:
https://doi.org/10.1016/j.jmsy.2020.04.002
(http://www.sciencedirect.com/science/article/pii/S0278612520300467)
◆ Rui Li, Wim
J.C. Verhagen, Richard Curran, Toward a methodology of requirements
definition for prognostics and health management system to support aircraft
predictive maintenance, Aerospace Science and Technology, Volume 102, 2020.
DOI:
https://doi.org/10.1016/j.ast.2020.105877
(http://www.sciencedirect.com/science/article/pii/S1270963820305599)
◆ Rui Li, Wim
J.C. Verhagen, Richard Curran, A systematic methodology for Prognostic and
Health Management system architecture definition, Reliability Engineering
& System Safety, Volume 193, 2020
DOI:
https://doi.org/10.1016/j.ress.2019.106598
(http://www.sciencedirect.com/science/article/pii/S0951832018315084)
◆ Behnoush
Rezaeianjouybari, Yi Shang, Deep learning for prognostics and health management:
State of the art, challenges, and opportunities, Measurement, Volume 163, 2020
DOI:
https://doi.org/10.1016/j.measurement.2020.107929
(http://www.sciencedirect.com/science/article/pii/S026322412030467X)
◆ Jae-Cheon Jung,
Adebena Oluwasegun, The application of machine learning for the Prognostics
and Health Management of control element drive system, Nuclear Engineering
and Technology, 2020
DOI:
https://doi.org/10.1016/j.net.2020.03.028
(http://www.sciencedirect.com/science/article/pii/S1738573319308654)
◆ Yubin Pan,
Rongjing Hong, Jie Chen, Weiwei Wu, A hybrid DBN-SOM-PF-based prognostic
approach of remaining useful life for wind turbine gearbox, Renewable Energy,
Volume 152, 2020
DOI:
https://doi.org/10.1016/j.renene.2020.01.042
(http://www.sciencedirect.com/science/article/pii/S0960148120300471)
◆ Olga Fink, Qin
Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe, Potential,
challenges and future directions for deep learning in prognostics and health
management applications, Engineering Applications of Artificial Intelligence,
Volume 92, 2020
DOI:
https://doi.org/10.1016/j.engappai.2020.103678
(http://www.sciencedirect.com/science/article/pii/S0952197620301184)
◆ Zong Meng, Jing
Li, Na Yin, Zuozhou Pan, Remaining useful life prediction of rolling bearing
using fractal theory, Measurement, Volume 156, 2020.
DOI:
https://doi.org/10.1016/j.measurement.2020.107572
(http://www.sciencedirect.com/science/article/pii/S0263224120301093)
◆ William Baker,
Steven Nixon, Jeffrey Banks, Karl Reichard, Kaitlynn Castelle, Degrader
Analysis for Diagnostic and Predictive Capabilities: A Demonstration of
Progress in DoD CBM+ Initiatives, Procedia Computer Science, Volume 168, 2020
DOI:
https://doi.org/10.1016/j.procs.2020.02.253
(http://www.sciencedirect.com/science/article/pii/S1877050920303926)
◆ Wihan Booyse,
Daniel N. Wilke, Stephan Heyns, Deep digital twins for detection, diagnostics
and prognostics, Mechanical Systems and Signal Processing, Volume 140, 2020.
DOI:
https://doi.org/10.1016/j.ymssp.2019.106612
(http://www.sciencedirect.com/science/article/pii/S0888327019308337)
◆ Sheng Xiang, Yi
Qin, Caichao Zhu, Yangyang Wang, Haizhou Chen, Long short-term memory neural
network with weight amplification and its application into gear remaining
useful life prediction, Engineering Applications of Artificial Intelligence, Volume
91, 2020.
DOI:
https://doi.org/10.1016/j.engappai.2020.103587
(http://www.sciencedirect.com/science/article/pii/S0952197620300634)
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