EFFECT OF THE PRESENCE OF STUMPS ON NO-FAILURE PERFORMANCE OF PEAT HARVESTING MACHINES

The article presents the results of a study on the impact of deposit stumps on the reliability of peat machines. The study was conducted in JSC «Vasilevsky mokh». The results of the study show failure flow statistics of main technological machines: cleaning, milling drums, agitators and swath collectors. Each type of machine has a number of failures, calculated MTBF, mean square deviation between failures and mean recovery time taking into account repair expectation time. Based on the research results the authors offer a classification of peat machine failures according to complexity and duration of their elimination. Failures are classified by 3 groups of complexity. The first group of complexity includes failures that could be fixed by operators of technological machines with recovery labor intensity up to 2 hours. The second group of complexity includes failures that require a repair team with recovery labor intensity up to 8 hours to eliminate them. The third group of complexity includes failures that require towing technological machines from a deposit to repair; their recovery labor intensity is more than 8 hours. It was found that technological machine operation on more than 2% volume stump deposits has resulted in a significant increase in a failure rate of milling drums, agitators and swath collectors, which cooperate with the peat deposit actively during the process. The failure flow almost has not changed for cleaning machines that interact with processed peat. The article proves that deposit stump recordkeeping during planning and maintenance of peat machines helps to improve production efficiency through full using of days with productive weather conditions.

Keywords

Peat machines, reliability, non-failure operation, repair, classification of failures.

Issue number: 12
Year: 2017
ISBN:
UDK: 622.331.002.5
DOI: 10.25018/0236-1493-2017-12-0-139-145
Authors: Gorlov I. V., Rakhutin M. G.

About authors: Gorlov I.V., Doctor of Technical Sciences, Assistant Professor, e-mail: gorloviv@yandex.ru, Tver State Technical University, 170026, Tver, Russia, Rakhutin M.G., Doctor of Technical Sciences, Professor, e-mail: mtm98@yandex.ru, Mining Institute, National University of Science and Technology «MISiS», 119049, Moscow, Russia.

REFERENCES: 1. Wang J. J., Jing Y. Y., Zhang C. F., Zhao J. H. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renew. Sustain. Energy Rev. 2009. Vol. 13. No. 9. Pp. 2263-2278. DOI: 10.1016/j.rser.2009.06.021
2. Wang J. J., Jing Y. Y., Zhang C. F., Shi G. H., Zhang X. T. A fuzzy multi-criteria decisionmaking model for trigeneration system. Energy Policy. 2008. Vol. 36. No. 10. Pp. 3823-3832. DOI: 10.1016/j.enpol.2008.07.002
3. Rakhutin M. G. Ugol'. 2006, no 5, pp. 44-46.
4. Mikhaylov A. V., Ivanov S. L., Gabov V. V. Vestnik Permskogo natsional'nogo issledovatel'skogo politekhnicheskogo universiteta. Geologiya. Neftegazovoe i gornoe delo. 2015, no 14, pp. 82-91.
5. Mikhaylov A. V., Ivanov S. L., Bondarev Yu. Yu. Nauchno-tekhnicheskie vedomosti SPbGPU. 2014, no 3 (202), pp. 229-235.
6. Khazanovich G. Sh., Chernykh V. G., Voronova E. Yu., Otrokov A. V. Gornoe oborudovanie i elektromekhanika. 2013, no 4, pp. 20-24.
7. Mikhaylov A. V., Ivanov S. L., Bol'shunov A. V., Kremcheev E. A. Zapiski Gornogo instituta. 2013. T. 200, pp. 226-230.
8. Spravochnik po torfu. Pod. red. A. V. Lazareva, S. S. Korchunova (Reference book on peat. Lazarev A. V. , Korchunov S. S. (Eds.)), Moscow, Nedra, 1982, 760 p.
9. Lemeshko B. Yu. Statisticheskiy analiz odnomernykh nablyudeniy sluchaynykh velichin (Statistical analysis of one-dimensional observations of random quantities), Novosibirsk, NGTU, 1995, 125 p.
10. Lin G. T.R., Shen Y. C. A collaborative model for technology evaluation and decisionmaking.
J. Sci. Indus. Res. 2010. Vol. 69. No. 2. Pp. 94-100.
11. Gorlov I. V., Poletaeva E. V. Gornyy informatsionno-analiticheskiy byulleten'. 2013, no 10, pp. 218-222.
12. Fang, Y., Lian, H., Liang, H., Ruppert D. Variance function additive partial linear models. Electronic Journal of Statistics. 2015. 9(2):2793-2827.
Mining World Russia
Subscribe for our dispatch