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2015 | 5 | 1667--1672
Tytuł artykułu

Supervised Context Classification Methods for an Industrial Machinery

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper describes a method of supervised context classification for an industrial machinery. The main objective of this study is to compare single and ensemble classifiers in order to classify groups of contexts which are based on an operating state of the device. The applied research was conducted with the assumption that only classic and well-practised classification methods would be adopted. The comparison study was carried out using real data recorded from an industrial machinery working underground in a mine in Poland. The achieved results confirm the effectiveness of the proposed approach and also show its limitations.(original abstract)
Rocznik
Tom
5
Strony
1667--1672
Opis fizyczny
Twórcy
  • Silesian University of Technology
Bibliografia
  • F. Caccavale and L. Villani, Fault Diagnosis and Fault Tolerance for Mechatronic Systems: Recent Advances, ser. Springer Tracts in Advanced Robotics. Springer Berlin/Heidelberg, 2003. [Online]. Available: http://dx.doi.org/10.1007/3-540-45737-2
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  • P. F. Odgaard and J. Stoustrup, "Results of a Wind Turbine FDI Competition," in Proceedings of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, A. Zaragoza, Ed., Aug. 2012, pp. 102-107. [Online]. Available: http://dx.doi.org/10.3182/20120829-3-mx-2028.00015
  • P. D. Turney, "Exploiting context when learning to classify," in Proceedings of the European Conference on Machine Learning, ser. ECML '93. London, UK, UK: Springer-Verlag, 1993. ISBN 3-540- 56602-3 pp. 402-407. [Online]. Available: http://dl.acm.org/citation. cfm?id=645323.649588
  • P. Turney, "The management of context-sensitive features: A review of strategies," in Proceedings of the ICML-96 Workshop on Learning in Context-Sensitive Domains, 1996, pp. 60-65. [Online]. Available: http://dx.doi.org/10.1.1.51.3784
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  • J. A. Jakubczyc, "Contextual classifier ensembles." in BIS, ser. Lecture Notes in Computer Science, W. Abramowicz, Ed., vol. 4439. Springer, 2007, pp. 562-569. [Online]. Available: http: //dx.doi.org/10.1007/978-3-540-72035-5_44
  • D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert, "Contextual classification with functional max-margin markov networks." in CVPR. IEEE, 2009, pp. 975-982. [Online]. Available: http://dx.doi.org/10. 1109/CVPRW.2009.5206590
  • Z. Ghahramani, "Hidden markov models." River Edge, NJ, USA: World Scientific Publishing Co., Inc., 2002, ch. An Introduction to Hidden Markov Models and Bayesian Networks, pp. 9-42.
  • R. Daly, Q. Shen, and J. S. Aitken, "Learning bayesian networks: approaches and issues." Knowledge Eng. Review, vol. 26, no. 2, pp. 99-157, 2011. [Online]. Available: http://dx.doi.org/10.1017/ S0269888910000251
  • A. Lile, "Analyzing e-learning systems using educational data mining techniques," Mediterranean Journal of Social Sciences, vol. 2, no. 3, pp. 403-419, 2011. [Online]. Available: http: //dx.doi.org/10.5901/mjss.2011.v2n3p403
  • F. Akthar and C. Hahne, RapidMiner 5, Operator Reference. www.rapid-i.com, 2012.
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  • A. Oksan Altun, I. Yilmaz, and M. Yildirim, "A short review on the surficial impacts of underground mining." Scientific Research and Essays, vol. 21, no. 5, pp. 3206-3212, 2010.
  • G. A. Einicke, J. C. Ralston, C. O. Hargrave, D. C. Reid, and D. W. Hainsworth, "Longwall mining automation an application of minimumvariance smoothing." IEEE Control Systems, vol. 28, no. 6, pp. 28-37, 2008. [Online]. Available: http://dx.doi.org/10.1109/MCS.2008.92928
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.ekon-element-000171422364

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