Summary
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Schalkoff, R.J. (1992). Pattern Recognition: Statistical, Structural and Neural Approaches. Jhon Wiley and Sons, Inc.
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.
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.
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.
Rumelhart, D.E., McClelland, J.L. & PDP Res. Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Bradford Book. MIT Press. Cambridge.
Kung, S.Y. (1993) Digital Neural Networks. PTR Prentice Hall. Englewood Cliff, New Jersey.
Freeman, J.A. & Skapura, D.M. (1991). Neural Networks. Algorithms, Applications and Programming Techniques. Addison Wesley Pub. Co. Massachusetts. U.S.A.
Rumelharht, D.E., McClelland, J.L. & P.D.P Res. Group. (1986). Parallel Distributed Processing. Vol. I. MIT Press
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.
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.
Duda, R. & Hart, P. (1973) Pattern Classification and Scene Analysis. Wiley & Sons. 1973
Schürmann, J. & Krebel, U. (1989) “Mustererkennung mit statistischen Benutzeroberfläche für einen Simulator konnektionistischer Netzwerke” Studienarbeit 746. IPVR. Universität Stuttgart.
Kohonen, T. (1988) Self-Organisation and Associative Memory. Springer Verlag. 1988.
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.
Braun, H. & Riedmiller, M. (1992) “Rprop: a fast adaptive learning algorithm”. In Proc. of the Int. Symposium on Computer and Information Science V II.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer-Verlag/Wien
About this paper
Cite this paper
Ortega, F., Ordieres, J.B., Menéndez, C., González Nicieza, C. (1995). Development of a Neural-based Diagnostic System to Control the Ropes of Mining Shifts. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-7091-7535-4_30
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive