Modern ICT and Mechatronic Systems in Contemporary Mining Industry

  • Wojciech Moczulski
  • Piotr Przystałka
  • Marek Sikora
  • Radosław Zimroz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9920)

Abstract

The paper deals with modern ICT techniques and systems, and mechatronic systems for mining industry, with particular attention paid to results achieved by the authors and their research groups. IT systems concern process and machinery monitoring, fault detection and isolation of processes and machinery, and assessment of risk and hazards in mining industry. Furthermore, innovative applications of AI methods are addressed, including pattern recognition and interpretation for process control, classification of seismic events, estimating loads of conveyors, and the others. Special attention is paid to applications of mechatronic solutions, such as: unmanned working machinery and longwalls in coal mines, and specialised robots for basic work. Mobile robots for inspecting areas of mines affected by catastrophes are presented, too. Moreover, recent communication solutions for collision avoidance, localisation of mining machinery, and wireless transmission are addressed. The paper concludes with most likely development of ICT and mechatronic systems for mining industry.

Keywords

ICT in mining industry Risk and hazards assessment Mechatronic working systems for mines Robotized inspection 

References

  1. 1.
    Bartelmus, W., Zimroz, R.: Vibration condition monitoring of planetary gearbox under varying external load. Mech. Syst. Signal Proc. 23(1), 246–257 (2009). Special Issue: Non-linear Structural DynamicsCrossRefGoogle Scholar
  2. 2.
    Bartkowiak, A., Zimroz, R.: Outliers analysis and one class classification approach for planetary gearbox diagnosis. J. Phys. Conf. Ser. 305(1), 012031 (2011)CrossRefGoogle Scholar
  3. 3.
    Bartkowiak, A., Zimroz, R.: Dimensionality reduction via variables selection - linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox. Appl. Acoust. 77, 169–177 (2014)CrossRefGoogle Scholar
  4. 4.
    Brzychczy, E.: The intelligent computer-aided support in designing mining operations at underground hard coal mines. In: 23th World Mining Congress, August 11–15 2013, Montreal (2013)Google Scholar
  5. 5.
    Cempel, C.: Multidimensional condition monitoring of mechanical systems in operation. Mech. Syst. Signal Process. 17(6), 1291–1303 (2003)CrossRefGoogle Scholar
  6. 6.
    Chekushina, E.V., Vorobev, A.E., Chekushina, T.V.: Use of expert systems in the mining. Middle-East J. Sci. Res. 18(1), 1–3 (2013)Google Scholar
  7. 7.
    Cholewa, W.: Expert systems in technical diagnostics. In: Korbicz, J., Kowalczuk, Z., Kościelny, J., Cholewa, W. (eds.) Fault Diagnosis, pp. 591–631. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Cioch, W., Knapik, O., Leśkow, J.: Finding a frequency signature for a cyclostationary signal with applications to wheel bearing diagnostics. Mech. Syst. Signal Process. 1(38), 55–64 (2013)CrossRefGoogle Scholar
  9. 9.
    COIG. http://www.coig.pl/en/mining, (as of Aug. 2016)
  10. 10.
    CSIRO. Mining safety and automation. http://www.csiro.au/en/Research/EF/Areas/Coal-mining/Mining-safety-and-automation, (as of Aug. 2016)
  11. 11.
  12. 12.
    EIT. http://eitrawmaterials.eu/, (as of Aug. 2016)
  13. 13.
    EU-Robotics. https://eu-robotics.net/, (as of Aug. 2016)
  14. 14.
    Golak, S., Wieczorek, T.: Koncepcja system ekspertowego do oceny i poprawy ekoefektywności kopalń (in Polish). Studia Informatica 116(2), 213–222 (2014)Google Scholar
  15. 15.
    Hofmann, M., Klinkenberg, R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman & Hall/CRC, Boca Raton (2013)Google Scholar
  16. 16.
  17. 17.
    Janusz, A., Sikora, M., Wróbel, L., Stawicki, S., Grzegorowski, M., Wojtas, P., Slezak, D.: Mining data from coal mines: IJCRS’15 data challenge. In: Yao, Y. (ed.) RSFDGrC 2015. LNCS, vol. 9437, pp. 429–438. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25783-9_38 CrossRefGoogle Scholar
  18. 18.
    Kabiesz, J.: Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks. Geotech. Geol. Eng. 24(5), 1131–1147 (2006)CrossRefGoogle Scholar
  19. 19.
    Kadlec, P., Gabrys, B., Strandt, S.: Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33(4), 795–814 (2009)CrossRefGoogle Scholar
  20. 20.
    Kalisch, M., Przystałka, P., Timofiejczuk, A.: A concept of meta-learning schemes for context-based fault diagnosis. In: XV International Technical Systems Degradation Conference, TSD International Conference, Liptovsky Mikulas, 30 March – 2 April 2016, pp. 113–114 (2016)Google Scholar
  21. 21.
    Kozielski, M., Sikora, M., Wróbel, Ł.: Disesor - decision support system for mining industry. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, vol. 5 of Annals of Computer Science and Information Systems, pp. 67–74. IEEE (2015)Google Scholar
  22. 22.
    Leica Geosystems. Autonomous and remote controlled mining. http://mining.leica-geosystems.com/news/all-news/autonomous-and-remote-controlled-mining, (as of Aug. 2016)
  23. 23.
    Liebowitz, J.: The Handbook of Applied Expert Systems. CRC Press LLC, Boca Raton (1997)MATHGoogle Scholar
  24. 24.
    Moczulski, W., Cyran, K., Januszka, M., Novak, P., Timofiejczuk, A.: Telerescuer - an innovative robotized system for supporting mining rescuers by inspecting roadways affected by catastrophes. In: 24th World Mining Congress (2016)Google Scholar
  25. 25.
    Przystałka, P., Moczulski, W., Timofiejczuk, A., Kalisch, M., Sikora, M.: Development of Expert System Shell for Coal Mining Industry. Springer, Heidelberg (2016)Google Scholar
  26. 26.
    RapidMiner: RapidMiner software website, August 2016. https://rapidminer.com/
  27. 27.
    Sáez, J.A., Krawczyk, B., Woźniak, M.: Analyzing the oversampling of different classes, types of examples in multi-class imbalanced datasets. Pattern Recogn. 57(C), 164–178 (2016)CrossRefGoogle Scholar
  28. 28.
    Sikora, M., Moczulski, W., Timofiejczuk, A., Przystałka, P., Ślȩzak, D.: DISESOR: An integrated shell decision support system for systems of monitoring processes, equipment and hazards. In: Mechanizacja, automatyzacja i robotyzacja w górnictwie, pp. 39–47 (2015)Google Scholar
  29. 29.
    Sikora, M., Przystałka, P., (eds.): Zintegrowany, szkieletowy system wspomagania decyzji dla systemów monitorowania procesów, urza̧dzeń i zagrożeń (in Polish). Publishing House of the Institute for Sustainable Technologies, National Research Institute, Radom, Poland (2016) (in print)Google Scholar
  30. 30.
    Sokołowski, J., Obuchowski, J., Madziarz, M., Wyłomańska, A., Zimroz, R.: Features based on instantaneous frequency for seismic signals clustering. J. VibroEng. 18(3), 1654–1667 (2016)CrossRefGoogle Scholar
  31. 31.
    Stefaniak, P., Zimroz, R., Bartelmus, W., Hardygóra, M.: Computerised decision-making support system based on data fusion for machinery systems management and maintenance. Appl. Mech. Mater. 683, 108–113 (2014)CrossRefGoogle Scholar
  32. 32.
    Wang, C., Wang, Z.: Design and implementation of safety expert information management system of coal mine based on fault tree. J. Softw. 5(10), 1114–1120 (2010)Google Scholar
  33. 33.
    Xu, X., Dou, L., Lu, C., Zhang, Y.: Frequency spectrum analysis on micro-seismic signal of rock bursts induced by dynamic disturbance. Min. Sci. Technol. 20(5), 682–685 (2010)Google Scholar
  34. 34.
    Yingxu, Q., Hongguo, Y.: Design and application of expert system for coal mine safety. In: Second IITA International Conference on Geoscience and Remote Sensing (2010)Google Scholar
  35. 35.
    Zimroz, R., Hardygóra, M., Błażej, R.: Maintenance of belt conveyor systems in Poland - an overview. In: Proceedings of the 12th International Symposium Continuous Surface Mining - Aachen, pp. 21–30. Springer, Heidelberg (2015)Google Scholar
  36. 36.
    Zimroz, R., Wodecki, J., Król, R., Andrzejewski, M., Śliwiński, P., Stefaniak, P.: Self-propelled Mining Machine Monitoring System - Data Validation, Processing and Analysis. Springer, Heidelberg (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Wojciech Moczulski
    • 1
    • 2
  • Piotr Przystałka
    • 1
  • Marek Sikora
    • 3
    • 4
  • Radosław Zimroz
    • 5
    • 6
  1. 1.Institute of Fundamentals of Machinery DesignSilesian University of TechnologyGliwicePoland
  2. 2.SkyTech Research Sp. z o.o.GliwicePoland
  3. 3.Institute of InformaticsSilesian University of TechnologyGliwicePoland
  4. 4.Institute of Innovative Technology EMAGKatowicePoland
  5. 5.Faculty of Geoengineering, Mining and GeologyTechnical University of WrocławWrocławPoland
  6. 6.KGHM Cuprum Sp. z o.o. CBRWrocławPoland

Personalised recommendations