Development of a Neural-based Diagnostic System to Control the Ropes of Mining Shifts

  • F Ortega
  • J. B Ordieres
  • C Menéndez
  • C. González Nicieza
Conference paper


Although the application of neural networks in quality control and maintenance is growing quickly from last years, they are just an incipient technology in the Mining Industry. At the same time, maintenance of the shift is probably the most important matter in Mining, considering that the shift could be the only way out for people and material in a colliery.

The Area of Project Engineering of the University of Oviedo has designed a system to control the state of the wire ropes for extraction in coal shifts, based on the information supplied by three groups of electromagnetic sensors (Inductive and Hall-Effect) placed in a head around the rope when inspection is carried out.

The system involves the use of three parallel neural subnetworks which output is introduced in common final layers to be definitely classified. This system allows to detect internal broken wires and to prevent more serious defects before they occur, in such a way that the rope can be maintained in service during a longer period of time, with the necessary equilibrium between security, reliability and economy. If this system is placed permanently on the shift, the risk of unexpected failure of the wire rope should be decreased to the minimum.


Hide Layer Output Unit Wire Rope Overhead Crane Project Engineer 
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.
    Schalkoff, R.J. (1992). Pattern Recognition: Statistical, Structural and Neural Approaches. Jhon Wiley and Sons, Inc.Google Scholar
  2. 2.
    Menéndez, C., Ortega, F., González, C., Alvarez, A. (1993) “Aplicación de las redes neuronales al reconocimiento de patrones”. IX Congreso Nacional de Ingeniería de Proyectos.Valencia.Google Scholar
  3. 3.
    Ortega, F., Menéndez, C., Ariznavarreta, F., Taboada, J. (1993) “Proceso de Señal Procedente del Control Online de Cables de Grúas”. IX Congreso Nacional de Ingeniería de Proyectos.Valencia.Google Scholar
  4. 4.
    Ordieres, J.B., Ortega, F., Menéndez, C. & Alonso, E. (1994) “Aplicación de las redes neuronales al mantenimiento de cables de grúa”. XI Congreso Nacional de Ingeniería Mecánica.Valencia.Google Scholar
  5. 5.
    Rumelhart, D.E., McClelland, J.L. & PDP Res. Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Bradford Book. MIT Press. Cambridge.Google Scholar
  6. 6.
    Kung, S.Y. (1993) Digital Neural Networks. PTR Prentice Hall. Englewood Cliff, New Jersey.Google Scholar
  7. 7.
    Freeman, J.A. & Skapura, D.M. (1991). Neural Networks. Algorithms, Applications and Programming Techniques. Addison Wesley Pub. Co. Massachusetts. U.S.A.Google Scholar
  8. 8.
    Rumelharht, D.E., McClelland, J.L. & P.D.P Res. Group. (1986). Parallel Distributed Processing. Vol. I. MIT PressGoogle Scholar
  9. 9.
    Powell, M.J.D. (1987). “Radial Basis Functions for Multivariable Interpolation: a review”. In Algorithms for Approximation. J.C. Mason and M.G. Cox ( Ed. ). Oxford. 143–167.Google Scholar
  10. 10.
    Carpenter, G.A. & Grossberg, S (1987). “A masively Parallel Architecture for Selforganizing Neural Pattern Recognition Machine”. Computer Vision, Graphics and Image Processing. 37, 54–115.Google Scholar
  11. 11.
    Duda, R. & Hart, P. (1973) Pattern Classification and Scene Analysis. Wiley & Sons. 1973Google Scholar
  12. 12.
    Schürmann, J. & Krebel, U. (1989) “Mustererkennung mit statistischen Benutzeroberfläche für einen Simulator konnektionistischer Netzwerke” Studienarbeit 746. IPVR. Universität Stuttgart.Google Scholar
  13. 13.
    Kohonen, T. (1988) Self-Organisation and Associative Memory. Springer Verlag. 1988.Google Scholar
  14. 14.
    Falhman, S.E. (1988) “Faster learning variations on back-propagation: an empirical study”. In Sejnowski, T.J., Hinton, G.E. & Touretzky, D.S. (Ed.). Connectionist Model Summer School, San Mateo, CA. Morgan Kaufmann.Google Scholar
  15. 15.
    Braun, H. & Riedmiller, M. (1992) “Rprop: a fast adaptive learning algorithm”. In Proc. of the Int. Symposium on Computer and Information Science V II.Google Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • F Ortega
    • 1
  • J. B Ordieres
    • 1
  • C Menéndez
    • 2
  • C. González Nicieza
    • 3
  1. 1.Project Engineering AreaUniversidad de OviedoSpain
  2. 2.Applied Mathematics AreaUniversidad de OviedoSpain
  3. 3.Mining AreaUniversidad de OviedoSpain

Personalised recommendations