AUTHORS: Saki Gerassis, Julio F. García, Ángeles Saavedra, José E. Martín, Javier Taboada
Download as PDF
ABSTRACT: The power unit is the fundamental element of hydraulic excavators. Its actual technological evolution derives in a design complexity that makes it difficult either for mining constructors or engineers to predict accurately its failure. For this reason, the main objective of this work is to provide a suitable decision model to obtain the probability distribution that better reflects the fault occurrence on the power unit for mining excavators from a work management perspective. The proposed method relies on the probabilities for each fault typology in the power unit estimated from data of faults collected in different mining excavators throughout its operation life. An optimum maintenance strategy is modelled through an influence diagram in terms of repair costs and production losses, representing the direct and indirect costs engineers have to face when a machine breaks down. An interesting result is the identification of the probabilistic model that best reflects the estimation of prior fault probabilities of the power unit elements. Surprisingly, indirect costs due to lack of production are found to be about 4.5 times bigger than direct costs, reflecting the necessity for a maintenance strategy capable to reduce faults in the early stages avoiding costs to become expansive over time. The application of this decision model helps to minimize production losses at the same time engineers gain knowledge about the risk attitudes that boost an efficient management of uncertainties involved with the severity and time of appearance of certain types of faults.
KEYWORDS: Decision making, hydraulic excavator, maintenance, systems reliability, mining engineering, risk tolerance, power unit.
REFERENCES:
[1] M. Haga, W. Hiroshi, K. Fujishima, Digging control System for hydraulic excavator, Mechatronics. Vol. 11, No. 6, 2001, pp. 665- 676.
[2] Q. Xiao, Q. Wang, Y. Zhang, Control strategies of power system in hybrid hydraulic excavator, Automation in Construction, Vol. 17, No. 4, 2008, pp. 361-367.
[3] L. Ge, L. Quan, X. Zhang, B. Zhao, J. Yang, Efficient improvement and evaluation of electric hydraulic excavator with speed and displacement variable pump, Energy Conservation and Management, Vol. 150, No. 15, 2017, pp. 62-71.
[4] N. Sedransk, J. Sedransk, Distinguishing among distributions using data from complex sample designs, Journal of the American Statistical Association, Vol. 74, No. 368, 1979, pp. 754-760.
[5] B. de Jonge, W. Klingenberg, R. Teunter, T. Tinga, Optimum maintenance strategy under uncertainty in the lifetime distribution, Reliability Engineering and Systems Safety, Vol. 133, 2015, pp. 59-67.
[6] R Core Team, A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2016.
[7] R. A. Howard, The foundations of decision analysis revisited. In Advances in decision analysis: from foundations to applications, ed. W. Edwards, R.F. Miles and D. Von Winterfeldt. Cambridge, UK: Cambridge University Press, 2007.
[8] BayesFusion, GeNIe Modeler, BayesFusion, LLc, Data Analytics, Mathematical Modelling, Decision Support. Pittsburgh, PA, 2017. http://www.bayesfusion.com/
[9] R. D. Shachter, Probabilistic inference and influence diagrams, Operations Research, Vol. 36, No. 4, 1988, pp. 589-604.
[10] Z. Pawlak, R. Sowinski, Rough set approach to multi-attribute decision analysis, European Journal of Operational Research, Vol. 72, No. 3, 1994, pp. 443-459.
[11] J. von Neumann, O. Morgenstern, Theory of games and economic behaviour, Princenton, NJ. Princenton University, 1953.