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Newsletter 14호 국내외 최신 대표 논문
Yang Hu, Xuewen Miao, Yong Si, Ershun Pan, Enrico Zio, Prognostics
and health management: A review from the perspectives of design, development
and decision, Reliability Engineering & System Safety, Volume 217, 2022, 108063,
ISSN 0951-8320,
https://doi.org/10.1016/j.ress.2021.108063.
(https://www.sciencedirect.com/science/article/pii/S0951832021005652)
Pier Carlo Berri, Matteo D.L. Dalla Vedova, Laura Mainini, Computational
framework for real-time diagnostics and prognostics of aircraft actuation
systems, Computers in Industry, Volume 132, 2021, 103523, ISSN 0166-3615,
https://doi.org/10.1016/j.compind.2021.103523.
(https://www.sciencedirect.com/science/article/pii/S0166361521001305)
Marcos Leandro Hoffmann Souza, Cristiano André da Costa, Gabriel
de Oliveira Ramos, Rodrigo da Rosa Righi, A feature identification method to
explain anomalies in condition monitoring, Computers in Industry, Volume 133,
2021, 103528, ISSN 0166-3615,
https://doi.org/10.1016/j.compind.2021.103528.
(https://www.sciencedirect.com/science/article/pii/S0166361521001354)
Concetta Semeraro, Mario Lezoche, Hervé Panetto, Michele Dassisti,
Digital twin paradigm: A systematic literature review, Computers in Industry,
Volume 130, 2021, 103469, ISSN 0166-3615,
https://doi.org/10.1016/j.compind.2021.103469.
(https://www.sciencedirect.com/science/article/pii/S0166361521000762)
Bianca Caiazzo, Mario Di Nardo, Teresa Murino, Alberto Petrillo,
Gianluca Piccirillo, Stefania Santini, Towards Zero Defect Manufacturing
paradigm: A review of the state-of-the-art methods and open challenges,
Computers in Industry, Volume 134, 2022, 103548, ISSN 0166-3615,
https://doi.org/10.1016/j.compind.2021.103548.
(https://www.sciencedirect.com/science/article/pii/S016636152100155X)
Yunhan Kim, Kyumin Na, Byeng D. Youn, A health-adaptive time-scale
representation (HTSR) embedded convolutional neural network for gearbox fault
diagnostics, Mechanical Systems and Signal Processing, Volume 167, Part B,
2022, 108575, ISSN 0888-3270,
https://doi.org/10.1016/j.ymssp.2021.108575.
(https://www.sciencedirect.com/science/article/pii/S0888327021009109)
Weihua Li, Ruyi Huang, Jipu Li, Yixiao Liao, Zhuyun Chen, Guolin
He, Ruqiang Yan, Konstantinos Gryllias, A perspective survey on deep transfer
learning for fault diagnosis in industrial scenarios: Theories, applications
and challenges, Mechanical Systems and Signal Processing, Volume 167, Part A,
2022, 108487, ISSN 0888-3270,
https://doi.org/10.1016/j.ymssp.2021.108487.
(https://www.sciencedirect.com/science/article/pii/S088832702100830X)
Bin Yang, Chi-Guhn Lee, Yaguo Lei, Naipeng Li, Na Lu, Deep partial
transfer learning network: A method to selectively transfer diagnostic
knowledge across related machines, Mechanical Systems and Signal Processing,
Volume 156, 2021, 107618, ISSN 0888-3270,
https://doi.org/10.1016/j.ymssp.2021.107618.
(https://www.sciencedirect.com/science/article/pii/S0888327021000133)
Meng Zhang, Tao Hu, Lifeng Wu, Guoqing Kang, Yong Guan, A method
for capacity estimation of lithium-ion batteries based on adaptive
time-shifting broad learning system, Energy, Volume 231,
2021, 120959, ISSN 0360-5442,
https://doi.org/10.1016/j.energy.2021.120959.
(https://www.sciencedirect.com/science/article/pii/S036054422101207X)
Mohamed Marei, Shirine El Zaatari, Weidong Li, Transfer learning
enabled convolutional neural networks for estimating health state of cutting
tools, Robotics and Computer-Integrated Manufacturing, Volume 71, 2021,
102145, ISSN 0736-5845,
https://doi.org/10.1016/j.rcim.2021.102145.
(https://www.sciencedirect.com/science/article/pii/S0736584521000302)
Unai Izagirre, Imanol Andonegui, Luka Eciolaza, Urko Zurutuza,
Towards manufacturing robotics accuracy degradation assessment: A
vision-based data-driven implementation, Robotics and Computer-Integrated
Manufacturing, Volume 67, 2021, 102029, ISSN 0736-5845,
https://doi.org/10.1016/j.rcim.2020.102029.
(https://www.sciencedirect.com/science/article/pii/S0736584520302404)
Yu Huang, Yufei Tang, James VanZwieten, Prognostics With
Variational Autoencoder by Generative Adversarial Learning, Volume 69, 2022,
856, ISSN 1557-9948,
https://doi: 10.1109/TIE.2021.3053882.
(https://ieeexplore.ieee.org/document/9339944)
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