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Method of Defining Diagnostic Features to Monitor the Condition of the Belt Conveyor Gearbox with the Use of the Legged Inspection Robot

  • Pawel StefaniakEmail author
  • Sergii Anufriiev
Conference paper
  • 197 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)

Abstract

This paper presents the results of constructing the inspection robot serving as a tool to define the conditions of technical infrastructure in deep mine. The project has been conducted as the part of the European “THING - subTerranean Haptic INvestiGator” project. The challenge of managing the dispersed deep mine machine park in the conveyor transport network has been discussed in the paper. One of the functions of the inspection is to evaluate the technical condition of the conveyor gearbox. Thus, the haptic robot leg has been designed to perform non-invasive vibration measurements on the gearbox housing. The inspection robot has been designed to consequently perform the necessary monitoring processes currently performed by human, which proves to be troublesome in the mining industry worldwide. The aim of the paper is to suggest the complete method of collecting field measurements and defining diagnostic features based on time-frequency analysis. Such an approach would facilitate full mobility and automatization of defining diagnostic features process with the robot, what is crucial in the harsh mining conditions.

Keywords

Inspection robot Deep mine Belt conveyor Gearbox Condition monitoring 

Notes

Acknowledgments

This work is a part of the project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780883.

References

  1. 1.
    Stefaniak, P., et al.: Some remarks on using condition monitoring for spatially distributed mechanical system belt conveyor network in underground mine-a case study. In: Fakhfakh, T., Bartelmus, W., Chaari, F., Zimroz, R., Haddar, M. (eds.) Condition Monitoring of Machinery in Non-stationary Operations, pp. 497–507. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-28768-8_51CrossRefGoogle Scholar
  2. 2.
    Stefaniak, P., Wodecki, J., Zimroz, R.: Maintenance management of mining belt conveyor system based on data fusion and advanced analytics. In: Timofiejczuk, A., Łazarz, B.E., Chaari, F., Burdzik, R. (eds.) ICDT 2016. ACM, vol. 10, pp. 465–476. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-62042-8_42CrossRefGoogle Scholar
  3. 3.
    Wei, Y., Wu, W., Liu, T., Sun, Y.: Study of coal mine belt conveyor state on-line monitoring system based on DTS. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 8924, Article number 89242I (2013)Google Scholar
  4. 4.
    Kuttalakkani, M., Natarajan, R., Singh, A.K., Vijayakumar, J., Arunan, S., Sarojini, L.: Sensor based effective monitoring of coal handling system (CHS). Int. J. Eng. Technol. 5(3), 2432–2435 (2013)Google Scholar
  5. 5.
    Eliasson, J., Kyusakov, R., Martinsson, P.-E., Eriksson, T., Oeien, C.: An Internet of Things approach for intelligent monitoring of conveyor belt rollers. In: 10th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies 2013, CM 2013 and MFPT 2013, vol. 2, pp. 1096–1104 (2013)Google Scholar
  6. 6.
    Sadhu, P.K., Chattopadhyaya, S., Chatterjee, T.K., Mittra, D.K.: Online monitoring and actuation for curing of rubber conveyor belts. J. Inst. Eng. (India) Mech. Eng. Div. 89, 31–35 (2008)Google Scholar
  7. 7.
    Keerthika, R., Jagadeeswari, M.: Coal conveyor belt fault detection and control in thermal power plant using PLC and SCADA. Int. J. Adv. Res. Comp. Eng. Technol. (IJARCET) 4, 1649–1652 (2015)Google Scholar
  8. 8.
    Sawicki, M., et al.: An automatic procedure for multidimensional temperature signal analysis of a SCADA system with application to belt conveyor components. Procedia Earth Planet. Sci. 15, 781–790 (2015)CrossRefGoogle Scholar
  9. 9.
    Zimroz, R., Hutter, M., Mistry, M., Stefaniak, P., Walas, K., Wodecki, J.: Why should inspection robots be used in deep underground mines? In: Widzyk-Capehart, E., Hekmat, A., Singhal, R. (eds.) Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection - MPES 2018, pp. 497–507. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-99220-4_42CrossRefGoogle Scholar
  10. 10.
    Käslin, R., Kolvenbach, H., Paez, L., Lika, K., Hutter, M.: Towards a passive adaptive planar foot with ground orientation and contact force sensing for legged robots. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, pp. 2707–2714 (2018)Google Scholar
  11. 11.
    Bednarek, J., Bednarek, M., Wellhausen, L., Hutter, M., Walas, K.: What Am I touching? Learning to classify terrain via haptic sensing. In: 2019 IEEE International Conference on Robotics and Automation (ICRA), Montreal (2019)Google Scholar
  12. 12.
    Stefaniak, P.: Modeling of exploitation processes of spatially distributed continuous transport system. Ph.D. thesis (2016). (in Polish)Google Scholar
  13. 13.
    Bartelmus, W.: Condition monitoring of open cast mining machinery. Oficyna Wydawnicza Politechniki Wrocławskiej (2006)Google Scholar
  14. 14.
  15. 15.
  16. 16.
    Zimroz, R.: Metoda diagnozowania wielostopniowych przekładni zębatych w napędach przenośników taśmowych z zastosowaniem modelowania. Ph.D. thesis, Wrocław (2002)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.KGHM Cuprum Research and Development CentreWrocławPoland

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