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Journal of Applied Mathematics
Volume 2014, Article ID 732104, 9 pages
http://dx.doi.org/10.1155/2014/732104
Research Article

Fault Detection and Diagnosis in Process Data Using Support Vector Machines

1Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Heilongjiang 150001, China
2Department of Engineering, Faculty of Engineering and Science, The University of Agder, 4898 Grimstad, Norway

Received 15 January 2014; Accepted 27 January 2014; Published 2 March 2014

Academic Editor: Weichao Sun

Copyright © 2014 Fang Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [12 citations]

The following is the list of published articles that have cited the current article.

  • Sufi Tabassum Gul, Munhal Imran, and Abdul Qayyum Khan, “An online incremental support vector machine for fault diagnosis using vibration signature analysis,” 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 1467–1472, . View at Publisher · View at Google Scholar
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  • Syed A. Taqvi, Lemma Dendena Tufa, Haslinda Zabiri, Abdulhalim Shah Maulud, and Fahim Uddin, “Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine,” Industrial & Engineering Chemistry Research, 2018. View at Publisher · View at Google Scholar
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