Analysis of Utilisation States and Reliability Parameters of Process-Line Configuration for Low-Quality Rock Deposits

• Autorzy: Bodziony Przemysław , Patyk Michał

Identyfikatory

DOI

10.12913/22998624/138765

• Języki publikacji: EN

Abstrakty

EN

The production performance of mining and also construction equipment (like haul trucks, excavators, loaders, crushing and screening equipment) depends on its availability and utilisation states. Thus, it is necessary to determine the mean availability and structure utilisation of machinery systems with the aim to improve the same. At the same time, a detailed analysis of machinery operation data can result in a more effective management of mining plant operations and the mining process itself. Determination of operational state indices and their individual components allows to start preventive actions, resulting in the improvement of the organisation of work of the entire mine machinery system. Moreover, the future technical state of machines operated in surface mining is closely related to the current state and depends also on the events that have occurred in the extraction system. The analysis of dependability parameters can have a significant impact on decision-making, related both to the processes of use and maintenance, and, primarily, to all main operational processes of the mine. Different mines follow different terms and maintain different information. The only common information in most of them concerns the availability and utilisation of mining and construction equipment, which might be useful. There is a need to develop proper feedback and to define terms, factors and indices related to mining equipment. Below follows an analysis of selected operational parameters and the mean availability in the process of using the mining and construction equipment. This type of analyses help determine the operational potential of every element in the engineering system, understood as the ability to perform work processes. The analyses also illustrate how the reliability parameters change with the durability of the operated machinery. This makes it possible to identify the machines with the highest and lowest damage rates and such analyses can form the basis for the creation of optimised machine systems.

Słowa kluczowe

EN

utilisation; surface mining; construction industry; mining equipment; failure intensity; reliability parameters; construction equipment

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2021
• Tom: Vol. 15, no 3
• Strony: 126--133
• Opis fizyczny: Bibliogr. 26 poz., fig.

Twórcy

autor

Bodziony Przemysław

• AGH University of Science and Technology, Faculty of Civil Engineering and Resource Management, Al. Mickiewicza 30, PL-30-059 Cracow, Poland.

• https://orcid.org/0000-0003-2466-9684

autor

Patyk Michał

• AGH University of Science and Technology, Faculty of Civil Engineering and Resource Management, Al. Mickiewicza 30, PL-30-059 Cracow, Poland.

• https://orcid.org/0000-0002-0698-5854

Bibliografia

• 1. Bodziony P., Patyk M., Kasztelewicz Z. Analysis of operating states of haul trucks used in surface mining. Journal of KONES: Powertrain and Transport. 2018;25(2):53-58.
• 2. Sinha R.S., Mukhopadhyay A.K. Failure rate analysis of jaw crusher using Weibull model. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 2017;231(4):760-772. https://doi. org/10.1177%2F0954408916636922
• 3. Allahkarami Z., Sayadi A.R., Ghodrati B. Identifying the mixed effects of unobserved and observed risk factors on the reliability of mining hauling system. International Journal of Systems Assurance Engineering and Management. 2021;12(2):281– 289. https://doi.org/10.1007/s13198-021-01073-3
• 4. Hall R.A., Daneshmend L.K. Reliability modelling of surface mining equipment: Data gathering and analysis methodologies. International Journal of Surface Mining, Reclamation and Environment. 2003;17(3):139155. https://doi.org/10.1076/ijsm.17.3.139.14773
• 5. Hall R.A., Daneshmend L.K., Lipsett M.G., Wong J. Reliability analysis as a tool for surface mining equipment evaluation and selection. CIM Bulletin. 2000;93(1044):78-82.
• 6. Singh A., Rai P., Sharma N., Pandey J. Failure Analysis and Performance Improvement Using Surface Miner on Field Breakdown Data: A case study. Industrial Engineering Journal. 2019;12(9). https://doi.org/10.26488/iej.12.9.1198
• 7. Morad A.M., Pourgol-Mohammad M., Sattarvand J. Application of reliability-centered maintenance for productivity improvement of open pit mining equipment: Case study of Sungun Copper Mine. Journal of Central South University. 2014;21(6):2372-2382. https://doi.org/10.1007/ s11771-014-2190-2
• 8. Stuchlý V., Poprocký R., Kaczmarek M. Reliability evaluation as a means of increasing the efficiency of equipment maintenance. Adv. Sci. Technol. Res. J. 2016;10(32):40-46. https://doi. org/10.12913/22998624/65109
• 9. Esmaeili M., Bazzazi A.A., Borna S. Reliability analysis of a fleet of loaders in sangan iron mine. Archives of Mining Sciences. 2011;56(4):629-640.
• 10. Nabizadeh M.A., Jalali S.M.E., Soltani A., Khademian A., Barabady J. Reliability and Maintainability Analysis of Material Handling Machinery in Golegohar Mine No. 1. In: Proc. of 4th International Conference on System Reliability and Safety, ICSRS, Rome, Italy 2019, 224-229. https://doi. org/10.1109/ICSRS48664.2019.8987634
• 11. Kumar U., Klefsjö B., Granholm S. Reliability investigation for a fleet of load haul dump machines in a Swedish mine. Reliability Engineering and System Safety. 1989;26(4):341-361. https://doi. org/10.1016/0951-8320(89)90004-5
• 12. Barabady J., Kumar U. Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran. Reliability Engineering and System Safety. 2008;93(4):647-653. https://doi.org/10.1016/j.ress.2007.10.006
• 13. Sinha R.S., Mukhopadhyay A.K. Reliability centered maintenance of cone crusher: a case study. International Journal of Systems Assurance Engineering and Management. 2015;6(1):32-35. https:// doi.org/10.1007/s13198-014-0240-7
• 14. Bodziony P., Patyk M., Kasztelewicz Z. Multi-criteria decision making for the choice equipment in mining with application of AHP method. New Trends in Production Engineering. 2019;2:404-415.
• 15. Bentley J.P. Introduction to reliability and Quality engineering. Addison-Wesley; 1999.
• 16. Hall R.A., Daneshmend L.K. Evaluation of reliability and availability of surface mining equipment. Journal of Mines, Metals and Fuels. 2002;50(10-11):395-400.
• 17. Gbadam E., Awuah-Offei K., Frimpong S. Investigation into mine equipment subsystem availability and reliability data modeling using discrete event simulation. In: Proc. of 37th International Symposium Application of Computers and Operations Research in the Mineral Industry, APCOM, United States 2015, 544-552.
• 18. Hebda M. Eksploatacja samochodów. Instytut Technologii Eksploatacji – PIB; 2005.
• 19. Hebda M., Janicki D. Trwałość i niezawodność samochodów w eksploatacji. Wydawnictwo Komunikacji i Łączności WKŁ; 1977.
• 20. Migdalski J. Inżynieria niezawodności. ZETOM; 1992.
• 21. Liu T., Zhang Z., Pan H. Failure mode effect and criticality analysis of the underground mining environment detector test system. International Journal of Applied Environmental Sciences. 2013;8(19):2441-2448.
• 22. Yiqiang W., Yazhou J., Weiwei J. Early failure analysis of machining centers: a case study. Reliability Engineering and System Safety. 2001;72.
• 23. Bebbington M., Lai C.D., Zitikis R. A flexible Weibull extension. Reliability Engineering and System Safety. 2007;92(6):719-726. https://doi. org/10.1016/j.ress.2006.03.004
• 24. ReliaSoft Corporation. Life Data Analysis Reference. Weibull++ 7. Reliasoft Publishing. Tucson AZ, USA 2008.
• 25. Bodziony P., Kudelski R., Patyk M., Kasztelewicz Z. Use of Artificial Neural Networks for the Estimated Prediction of Haul Trucks Operating States. In: Proc. of 14th International Scientific Conference: Computer Aided Engineering, Poland 2019.
• 26. Żółtowski B., Cempel C. Inżynieria diagnostyki maszyn. ITeE; 2005.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).