Abstract
The paper presents a new approach of wall conveyor engines diagnosis. A wall conveyor is an essential device in coal mines. Its work is usually represented by three time series of current values of three conveyor engines. The startup of the conveyor is the phase with the maximal observed load during its work cycle. In the research, each startup is described with almost twenty variables. On the basis of 1000 real monitored startups, a set of association rules was inducted. On the basis of the further rules analysis and interpretation, a set of almost 50 rules was selected to the diagnosis system. The proposed diagnosis system compares the quality (precision) of each association rule from a selected subset—the precision evaluated on the representative data—with the precision of the same rule, evaluated on newly detected startups.
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References
Bartelmus, W.: Condition monitoring of open cast mining machinery. Wroclaw University of Technology Press, Wroclaw (2006)
DEMKop System: http://www.somar.com.pl/katalog-wyrobow/demkop,18,2,71
Ga̧sior, S.: Diagnosis of longwall chain conveyor. Przegla̧d Górniczy 57(7–8), 33–36 (2001)
Głowacz, A.: Diagnostics of direct current machine based on analysis of acoustic signals with the use of symlet wavelet transform and modified classifier based on words. Eksploatacja i Niezawodność—Maint. Reliab. 16(4), 554–558 (2014)
Głowacz, A., Głowacz, A., Korohoda, P.: Recognition of monochrome thermal images of synchronous motor with the application of binarization and nearest mean classifier. Arch. Metall. Mater. 59(1), 31–34 (2014)
Kacprzak, M., Kulinowski, P., Wȩdrychowicz, D.: Computerized information system used for management of mining belt conveyors operation. Eksploatacja i Niezawodność—Maint. Reliab. 13(2), 81–93 (2011)
Karabadji, N., Seridi, H., Khelf, I., Azizi, N., Boulkroune, R.: Improved decision tree construction based on attribute selection and data sampling for fault diagnosis in rotating machines. Eng. Appl. Artif. Intell. 35, 71–83 (2014)
Michalak, M., Sikora, M., Sobczyk, J.: Analysis of the longwall conveyor chain based on a harmonic analysis. Eksploatacja i Niezawodność—Maint. Reliab. 15(4), 332–336 (2013)
Siciński, K., Isakow, Z., Oset, K.: Monitoring of longwall coal-cutting machines’ work to improve the efficiency of the wall extraction. Mech. Autom. Min. Ind. 381(9), 113–120 (2002)
Webb, G.: Discovering associations with numeric variables. In: SIGKDD 2001, San Francisco, USA, pp. 383–388 (2001)
Webb, G.: Discovering significant patterns. Mach. Learn. 68(1), 1–33 (2007)
Wong, P., Yang, Z., Vong, C., Zhong, J.: Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing 128, 249–257 (2014)
Acknowledgments
The work was financially supported by POIG.02.03.01-24-099/13 grant: GeCONiI “Upper Silesian Center for Computational Science and Engineering”. The participation of the second author was supported by Polish National Centre for Research and Development (NCBiR) grant PBS2/B9/20/2013 in frame of Applied Research Programmes.
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Michalak, M., Sikora, B., Sobczyk, J. (2016). Diagnostic Model for Longwall Conveyor Engines. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_37
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DOI: https://doi.org/10.1007/978-3-319-23437-3_37
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