CONDITION MONITORING OF OFF-HIGHWAY TRUCK TIRES AT SUNGUN COPPER MINE USING NEURAL NETWORKS / MONITOROWANIE STANU TECHNICZNEGO OPON W CIĘŻKICH POJAZDACH TERENOWYCH EKSPLOATOWANYCH W KOPALNI MIEDZI SUNGUN, PRZY UŻYCIU SIECI NEURONOWYCH
Maintenance cost of the equipment is one of the most important portions of the operating expenditures in mines; therefore, any change in the equipment productivity can lead to major changes in the unit cost of the production. This clearly shows the importance and necessity of using novel maintenance methods instead of traditional approaches, in order to reach the minimum sudden occurrence of the equipment failure. For instance, the tires are costly components in maintenance which should be regularly inspected and replaced among different axles. The paper investigates the current condition of equipment tires at Sungun Copper Mine and uses neural networks to estimate the wear of the tires. The Input parameters of the network composed of initial tread depth, time of inspection and consumed tread depth by the time of inspection. The output of the network is considered as the residual service time ratio of the tires. The network trained by the feed-forward back propagation learning algorithm. Results revealed a good coincidence between the real and estimated values as 96.6% of correlation coefficient. Hence, better decisions could be made about the tires to reduce the sudden failures and equipment breakdowns.
Abou-Ali M.G., Khamis M., 2003. TIREDDX: an integrated intelligent defects diagnostic system for tire production and service. Expert Systems with Applications 24(3): 247-259.
Adetan D.A., Oladejo K. A., Fasogbon S. K., 2008. Redesigning the manual automobile tyre bead breaker. Technology in Society 30(2): 184-193.
Al-Garni A.Z., Jamal A., Ahmad A. M., Al-Garni A. M., Tozan M., 2006. Neural network-based failure rate prediction for De Havilland Dash-8 tires. Engineering Applications of Artificial Intelligence 19(6): 681-691.
Coast Tire & Auto Service, 2004. Visited May 2012, from http://www.coasttire.com/tires/tires.asp.
Coast Tire & Auto Service, 2004. Visited April 2012, from http://www.coasttire.com/tires/alignment.asp.
Demuth H., Beale M., Hagan M., 2009. Neural network MATLAB toolbox 6: User’s guide. The Math Works Inc., Natick.
Dunlop, 2005. Visited April 2012, from http://www.dunlop.ca/care/proper_inflation.html
Gholamnejad J., Tayarani N., 2010. Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Mining Science and Technology (China) 20(5): 727-733.
Goodyear, 1996. visited Agust 2012, from http://www.goodyear.eu/home_en/images/GYR%20databook.pdf.
Goodyear OFF-THE-ROAD TIERS, 1996. Tire maintenance manual, Visited October 2012, from http://www.goodyearotr.com/cfmx/web/otr/info/
Hoseinie S.H., Ataie M., Khalookakaei R., Kumar U., 2011. Reliability modeling of water system of longwall shearer machine. Archive of Mining Science 56(2): 291-302.
MoCulloch W.S., Pitts W., 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of mathematical biology 5(4): 115-133.
Mehrotra K., Mohan C.K., Ranka S., 1997. Elements of artificial neural networks, the MIT Press.
Michelin P., Zingraff R., 1996. Michelin truck tire service manual, Michelin.
Tawadrous A., Katsabanis P., 2007. Prediction of surface crown pillar stability using artificial neural networks. International journal for numerical and analytical methods in geomechanics 31(7): 917-931.
Wik A., Dave G., 2009. Occurrence and effects of tire wear particles in the environment - A critical review and an initial risk assessment. Environmental Pollution 157(1): 1-11.