Analysis of Deformation of Mining Chains Based on Motion Tracking

  • Marcin Michalak
  • Karolina Nurzyńska
  • Andrzej Pytlik
  • Krzysztof Pacześniowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)


This paper presents the possibilities of using a compact digital camera, which is amateur class of equipment, to a simple analysis of the deformation of mining chains during the tests performed at percussive, dynamic load. When the mining chain works during underground coal-bed exploitation, in the scraper conveyor, which is one of the elements of a longwall system, there could be observed frequent chains fractures due to its dynamic percussive load. It often occurs as a result of an emergency chain locking in the troughs of the chain conveyor. For the purpose of analysis special software was created. Thus it was possible to obtain at low cost, with the assumed accuracy of deformation measurement, deformations as a function of time, without installing on the monitored elements acceleration sensors (e.g. inductive, potentiometric), which were usually destroyed during the tests, mostly due to their low resistance to shock.


Corrosion Protection Motion Tracking Test Stand Reference Marker Scraper Conveyor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Polish Norm: PN-G-46701:1997 - Mining link chainsGoogle Scholar
  2. 2.
    THIELE: Mining products catalogGoogle Scholar
  3. 3.
    Anandan, P.: A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision 2(3), 283–310 (1989)CrossRefGoogle Scholar
  4. 4.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High Accuracy Optical Flow Estimation Based on a Theory for Warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Fleet, D.J., Jepson, A.D.: Computation of component image velocity from local phase information. International Journal of Computer Vision 5(1), 77–104 (1990)CrossRefGoogle Scholar
  6. 6.
    Heeger, D.: Optical flow using spatio-temporal filters. International Journal of Computer Vision 1(4), 279–302 (1988)CrossRefGoogle Scholar
  7. 7.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)CrossRefGoogle Scholar
  8. 8.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: DARPA Image Understanding Workshop, pp. 121–130 (1981)Google Scholar
  9. 9.
    Nagel, H.H.: Displacement vectors derived from 2nd order intensity variations in image sequences. Computer Vision, Graphics and Image Processing 21(1), 85–117 (1983)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Uras, S., Girosi, F., Verri, A., Torre, V.: A computational approach to motion perception. Biological Cybernetics 60(2), 79–87 (1988)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcin Michalak
    • 1
  • Karolina Nurzyńska
    • 1
  • Andrzej Pytlik
    • 1
  • Krzysztof Pacześniowski
    • 1
  1. 1.Central Mining InstituteKatowicePoland

Personalised recommendations