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Tytuł artykułu

Application of machine learning and soft computing techniques in monitoring systems' data analysis by example of dewater pumps monitoring system

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Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Application of machine learning method for creation of equipment diagnostic model is presented in the paper. Dewater pump working in abyssal mining pump station has been chosen as the illustrative example. In the second section, dewater pumps monitoring system is presented, and necessity of the pump diagnostic model creation is justified. Next sections present application of data clustering algorithm and algorithm of decision trees induction. Methods of reduction the get diagnostic model is also developed. The reduction leads to more legible data models. Results of analysis done for two different type of pumps are presented in the last part of the paper.
Rocznik
Strony
369--391
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • Institute of Computer Sciences, Silesian University of Technology, Gliwice, Poland, and Center for Mining Electrification and Automation EMAG, Katowice, Poland
Bibliografia
  • [1] T. AGOTNES, J. KOMOROWSKI and T. LOKEN: Taming large rule models in rough set approaches. Lecture Notes in Artificial Intelligence, 1704 (1999), 193-203.
  • [2] A. AN and N. CERCONE: Rule quality measures for rule induction systems: Description and evaluation. Computational Intelligence, 17(3), (2001), 409-424.
  • [3] J. C. BEZDEK and S. K. PAL (EDS.): Fuzzy models for pattern recognition. Methods that search for structures in data. IEEE Press, New York, 1992.
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  • [5] I. BRUHA: Quality of decision rules: Definitions and classification schemes for multiple rules. In: G. Nakhaeizadeh, C.C. Taylor (Eds.): Machine learning and statistics. The Interface. John Wiley and Sons, 1997.
  • [6] O. CASTILIO, P. MELIN, J. KACPRZYK and W. PEDRYCZ: Hybrid intelligent systems - Design and analysis. Studies in Fuzzines and Soft Computing. Springer-Verlag, Berlin, 2006.
  • [7] P. CICHOSZ: Learning systems. Wydawnictwo WNT , Warszawa, 2000, (in Polish).
  • [8] E. CZOGAŁA., J. ŁĘSKI: Fuzzy and neuro-fuzzy intelligent systems. Studies in Fuzzines and Soft Computing. Physica-Verlag, 2000.
  • [9] G. DRWAL and M. SIKORA: Induction of fuzzy ecision rules based upon rough sets theory. Proc. Int. Conf. on Fuzzy Systems, Budapest, Hungary, (2004), 1391-1397.
  • [10] U. M. FAYYAD, G. PIATETSKY-SHAPIRO and P. SMYTH: Advances in knowledge discovery and data mining. MIT Press, Cambridge Mass., 1996.
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  • [13] R. S. MICHALSKI: Pattern recognition as rule-guided inductive inference. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-2, (1980), 349-361.
  • [14] J. M. PENA, J. A. LOZANO and P. LARANAGA: An empirical comparison of four initialization methods for the k-means algorithm. Pattern Rec. Lett., 20 (1999), 1027-1040.
  • [15] R. QUINLAN: C4.5 Programs for machine learning. Morgan Kaufman Publishers, San Mateo, California, 1993.
  • [16] R. QUINLAN: www.rulequest.com
  • [17] S. L. SALZBERG: On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery, 1(3), (1997), 317-328.
  • [18] M. SIKORA and P. PROKSA: Algorithms for generation and filtration of approximate decision rules, using rule-related quality measures. Bulletin of Int. Rough Set Society, 5(1/2), (2001), (Proc. of the RSTGC 2001), 93-99.
  • [19] M. SIKORA and D. WIDERA: Identyfication of diagnostics states for dewater pumps working in abyssal mining pump stations. Proc. of the XV Int. Conf. On System Science, Wroclaw, Poland, (2004), 394-402
  • [20] M. SIKORA: An algorithm for generalization of decision rules by joining. Foundation on Computing and Decision Sciences, 30(3), (2005), 227-239.
  • [21] M. SIKORA: Fuzzy rules generation method for classification problems using rough sets and genetic algorithms. Lecture Notes in Artificial Intelligence, 3641 (2005), Springer-Verlag, Berlin Heidelberg, 383-391.
  • [22] M. SIKORA and M. KOZIELSKI: Hybrid data exploration methods to prediction tasks solving. Archives of Theoretical and Applied Informatics. 18(1) (2006), 57-74.
  • [23] M. SIKORA: Rule quality measures in creation and reduction of data role models. Lecture Notes in Artificial Intelligence. 4259 (2006), Springer-Verlag, Berlin, 716-725.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0042-0010
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