A support vector machine framework for fault detection in molecular pump

Yuan, X. L.; Kai, J.; Chen, Y.; Zuo, G. Z.; Zhuang, H. D.; Li, J. H.; Hu, J. S.

doi:10.1080/00223131.2022.2080124

A support vector machine framework for fault detection in molecular pump

Journal Article · 02 June 2022 · Journal of Nuclear Science and Technology (Tokyo)

DOI:https://doi.org/10.1080/00223131.2022.2080124· OSTI ID:2420545

^[1]; Kai, J. ^[2]; Chen, Y. ^[3]; Zuo, G. Z. ^[3]; Zhuang, H. D. ^[3]; Li, J. H.; Hu, J. S. ^[3]

Chinese Academy of Sciences, Institute of Plasma Physics, Hefei, China; OSTI
Anhui Polytechnic University, School of Electrical Engineering, Wuhu, China
Chinese Academy of Sciences, Institute of Plasma Physics, Hefei, China

Not provided.

Research Organization:: Johns Hopkins Univ., Baltimore, MD (United States); Princeton Univ., NJ (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0016553; AC02-09CH11466

OSTI ID:: 2420545

Journal Information:: Journal of Nuclear Science and Technology (Tokyo), Journal Name: Journal of Nuclear Science and Technology (Tokyo) Journal Issue: 1 Vol. 60; ISSN 0022-3131

Publisher:: Taylor & Francis

Country of Publication:: United States

Language:: English

References (18)

Innovative signal processing and data analysis methods on JET for control in the perspective of next-step devices Murari, A.; Vega, J.; Mazon, D. Nuclear Fusion, Vol. 50, Issue 5 https://doi.org/10.1088/0029-5515/50/5/055005	journal	May 2010
Diagnosis for PEMFC Systems: A Data-Driven Approach With the Capabilities of Online Adaptation and Novel Fault Detection Li, Zhongliang; Outbib, Rachid; Giurgea, Stefan IEEE Transactions on Industrial Electronics, Vol. 62, Issue 8 https://doi.org/10.1109/TIE.2015.2418324	journal	August 2015
Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis Liu, Ruonan; Yang, Boyuan; Zhang, Xiaoli Mechanical Systems and Signal Processing, Vol. 75 https://doi.org/10.1016/j.ymssp.2015.12.020	journal	June 2016
Disruption Prediction by Support Vector Machine and Neural Network with Exhaustive Search Yokoyama, Tatsuya; Sueyoshi, Takamitsu; Miyoshi, Yuya Plasma and Fusion Research, Vol. 13, Issue 0 https://doi.org/10.1585/pfr.13.3405021	journal	January 2018
A scalable fuzzy support vector machine for fault detection in transportation systems Liu, Jie; Zio, Enrico Expert Systems with Applications, Vol. 102 https://doi.org/10.1016/j.eswa.2018.02.017	journal	July 2018
Recognition of multiple partial discharge patterns by multi‐class support vector machine using fractal image processing technique Basharan, Vigneshwaran; Maria Siluvairaj, Willjuice Iruthayarajan; Ramasamy Velayutham, Maheswari IET Science, Measurement & Technology, Vol. 12, Issue 8 https://doi.org/10.1049/iet-smt.2018.5020	journal	November 2018
Fault Protection and Overload Diagnosis in a Regulated High-Voltage Power Supply Patel, Paresh; Sumod, C. B.; Thakkar, D. P. Fusion Science and Technology, Vol. 64, Issue 1 https://doi.org/10.13182/FST13-A17045	journal	July 2013
Vacuum and wall conditioning system on EAST Hu, J. S.; Wang, X. M.; Li, J. H. Fusion Engineering and Design, Vol. 84, Issue 12 https://doi.org/10.1016/j.fusengdes.2009.03.016	journal	December 2009
A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management Kandukuri, Surya Teja; Klausen, Andreas; Karimi, Hamid Reza Renewable and Sustainable Energy Reviews, Vol. 53 https://doi.org/10.1016/j.rser.2015.08.061	journal	January 2016
Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform Wang, Hongchao; Chen, Jin; Dong, Guangming Mechanical Systems and Signal Processing, Vol. 48, Issue 1-2 https://doi.org/10.1016/j.ymssp.2014.04.006	journal	October 2014
Fault diagnosis network design for vehicle on-board equipments of high-speed railway: A deep learning approach Yin, Jiateng; Zhao, Wentian Engineering Applications of Artificial Intelligence, Vol. 56 https://doi.org/10.1016/j.engappai.2016.10.002	journal	November 2016
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data Lei, Yaguo; Jia, Feng; Lin, Jing IEEE Transactions on Industrial Electronics, Vol. 63, Issue 5 https://doi.org/10.1109/TIE.2016.2519325	journal	May 2016
Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid Jindal, Anish; Dua, Amit; Kaur, Kuljeet IEEE Transactions on Industrial Informatics, Vol. 12, Issue 3 https://doi.org/10.1109/TII.2016.2543145	journal	June 2016
Fast and robust fault diagnosis for a class of nonlinear systems: detectability analysis Li, Linglai; Zhou, Donghua Computers & Chemical Engineering, Vol. 28, Issue 12 https://doi.org/10.1016/j.compchemeng.2004.07.023	journal	November 2004
Wavelets for fault diagnosis of rotary machines: A review with applications Yan, Ruqiang; Gao, Robert X.; Chen, Xuefeng Signal Processing, Vol. 96 https://doi.org/10.1016/j.sigpro.2013.04.015	journal	March 2014
An evolving approach to unsupervised and Real-Time fault detection in industrial processes Bezerra, Clauber Gomes; Costa, Bruno Sielly Jales; Guedes, Luiz Affonso Expert Systems with Applications, Vol. 63 https://doi.org/10.1016/j.eswa.2016.06.035	journal	November 2016
Electric load forecasting by using dynamic neural network Mordjaoui, Mourad; Haddad, Salim; Medoued, Ammar International Journal of Hydrogen Energy, Vol. 42, Issue 28 https://doi.org/10.1016/j.ijhydene.2017.03.101	journal	July 2017
Prediction of unusual plasma discharge by using Support Vector Machine Nakagawa, Shota; Hochin, Teruhisa; Nomiya, Hiroki Fusion Engineering and Design, Vol. 167 https://doi.org/10.1016/j.fusengdes.2021.112360	journal	June 2021

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