ENGINEERING MANAGEMENT OF GRAIN HARVESTER FAILURE MANAGEMENT UNDER TECHNOLOGY OF MAINTENANCE TECHNOLOGY
Abstract
The article discusses the feasibility of applying the normative complex of engineering management for adaptive maintenance technology in the storage of combine harvesters. The basis of experimental research is the working scientific hypothesis that the efficiency of machine use of combine harvesters largely depends on its reliability, in particular on the indicators of failure directly in the harvest process. When harvesting grain crops, it is necessary to ensure the working condition of combines during the normative agro-technical period. Therefore, the main characteristic feature of combine harvesters is characterized by reliability. It is assumed that the failure of combine harvesters for technical and technological reasons leads to downtime of the combines and, as a consequence, to the loss of part of the grain harvest. If the established normative agro-technical term of grain harvesting is exceeded, the specific losses of grain are 0.004…0.006 % for one hour of downtime. Analysis of statistical data on the technical condition of facilities on the basis of operational observations revealed possible patterns and causes of failures. The analysis of results of experimental researches with establishment of numerical values of indicators of failure of grain harvesters is carried out, namely, average time on the first failure, average time on failure, average number of failures, average time of elimination of failure, coefficient of variation of failures. Graphic interpretation of the dependence of failure indicators of combine harvesters is presented, namely, the density of failure of combines, the total number of failures in the process of combines, the total readiness factor of combine harvesters. The existence of the influence of the change in the average operating time on the failure in the process of operation of combines on the indicators of failure of the combine has been confirmed. The considered approach with a similar analysis allows to reasonably put forward requirements to the characteristics of maintenance technologies during storage of combine harvesters.
References
3. Corinne, B., & José, R. (2017). Estimating the Hurst parameter. Statistical Inference for Stochastic Processes. Springer Verlag, 10(1): 49–73.
4. Erokhin, M., Pastukhov, A., & Kazantsev, S. (2019). Operability assessment of drive shafts of John Deere tractors in operational parameters. Engineering for rural development 18: 28–33.
5. Gurcanli, E., Bilir, S., & Sevim, M. (2015). Activity based risk assessment and safety cost estimation for residential building construction projects. Safety Science 80: 1–12.
6. Gyansah, L., & Ansah, A. (2020). Fatigue crack initiation analysis in 1060 steel. Research journal of applied sciences engineering and technology 4(2): 319–325.
7. Hrynkiv, A., Rogovskii, I., Aulin, V., Lysenko, S., Titova, L., Zagurskіy, O., & Kolosok, I. (2020). Development of a system for determining the informativeness of the diagnosing parameters of the cylinder-piston group of the diesel engines in operation. Eastern-European Journal of Enterprise Technologies 3 (5(105)): 19−29. DOI: 10.15587/1729-4061.2020.206073.
8. Kalinichenko, D., & Rogovskii, I. (2017). Modeling technology in centralized technical maintenance of combine harvesters. TEKA 17(3): 93–102.
9. Khamidullina, E.A., Timofeeva, S.S., & Smirnov, G.I. (2017). Accidents in coal mining from perspective of risk theory. IOP Conference Series: Materials Science and Engineering 262: 012210.
10. Kuzmich, I.M., Rogovskii, I.L., Titova, L.L., & Nadtochiy, O.V. (2021). Research of passage capacity of combine harvesters depending on agrobiological state of bread mass. IOP Conference Series: Earth and Environmental Science 677: 052002. DOI: http://dx.doi.org/10.1088/1755-1315/677/5/052002.
11. Kuzmich, І.М., & Rogovskii, I.L. (2021). Engineering management of maintenance during storage of combine harvesters. TEKA. Journal of Agri-Food Industry 21(1): 53–60.
12. Kypris, O., Nlebedim, I., & Jiles, D. (2016). Measuring stress variation with depth using Barkhausen signal. Journal of Magnetism and Magnetic Materials – Science Direct 407: 377–395.
13. Najafi, P., Asoodar, M., Marzban, A., & Hormozi, M. (2015). Reliability analysis of agricultural machinery: A case study of sugarcane chopper harvester. AgricEngInt: CIGR Journal March 17(1)1: 158–165.
14. Nazarenko, I., Dedov, O., Bernyk, I., Rogovskii, I., Bondarenko, A., Zapryvoda, A., & Titova, L. (2020). Study of stability of modes and parameters of motion of vibrating machines for technological purpose. Eastern-European Journal of Enterprise Technologies 6 (7(108)): 71−79. DOI: https://doi.org/10.15587/1729-4061.2020.217747.
15. Nazarenko, I., Mishchuk, Y., Mishchuk, D., Ruchynskyi, M., Rogovskii, I., Mikhailova, L., Titova, L., Berezovyi, M., & Shatrov, R. (2021). Determiantion of energy characteristics of material destruction in the crushing chamber of the vibration crusher. Eastern-European Journal of Enterprise Technologies. 4(7(112)): 41–49. DOI: https://doi.org/10.15587/1729-4061. 2021.239292.
16. Nykyforchyn, H., Lunarska, E., & Tsyrulnyk, O. (2019). Environmentally assisted “in-bulk” steel degradation of long term service gas trunkline. Engineering Failure Analysis 17: 624-632.
17. Pisarenko, G., Voinalovych, O., Rogovskii, I., & Motrich, M. (2019). Probability of boundary exhaustion of resources as factor of operational safety for agricultural aggregates. Engineering for rural development 18: 291–298.
18. Rejovitzky, E., & Altus, E. (2013). On single damage variable models for fatigue. International Journal of Damage Mechanics 22(2) 2: 268–284.
19. Rogovskii, I. 2020. Algorithmicly determine the frequency of recovery of agricultural machinery according to degree of resource’s costs. Machinery & Energetics. Journal of Rural Production Research 11(1): 155–162.
20. Rogovskii, I., Titova, L., Novitskii, A., & Rebenko, V. (2019). Research of vibroacoustic diagnostics of fuel system of engines of combine harvesters. Engineering for rural development 18: 291–298.
21. Rogovskii, I.L., Titova, L.L., Voinash, S.A., Troyanovskaya, I.P., & Sokolova, V.A. (2021). Change of technical condition and productivity of grain harvesters depending on term of operation. IOP Conference Series: Earth and Environmental Science 720: 012110. DOI: https://doi.org/10.1088/1755-1315/720/1/012110.
22. Sánchez-Hermosilla, J., Rincón, V., & Páez, F. (2011). Field evaluation of a self-propelled sprayer and effects of the application rate on spray deposition and losses to the ground. Pest Management Science 67(8): 942–947.
23. Shih-Heng, T., Ming-Hsiang, S., & Wen-Pei, S. (2018). Development of digital image correlation method to analyse crack variations of masonry wall. Sadhana 6: 767–779.
24. Tyutrin, S. (2019). Improving reliability of parts of mounted mower according to monitoring results by fatigue gauges from tin foil. Engineering for rural development 18: 22–27.
25. Voinalovych, O., Hnatiuk, O., Rogovskii, I., & Pokutnii, O. (2019). Probability of traumatic situations in mechanized processes in agriculture using mathematical apparatus of Markov chain method. Engineering for rural development 18: 563–569.
26. Xi, L., & Songlin, Z. (2019). Changes in mechanical properties of vehicle components after strengthening under low-amplitude loads below the fatigue limit. Fatigue and Fracture of Engineering Materials and Structures 32(10): 847–855.
27. Zou, F., Kang, J., Xiao, M., & Ji, G. (2017). Hydrostatic driving system for self-propelled sprayer. Engineering Journal 26(3): 12–18. 28. Zubko, V., Sirenko, V., Kuzina, T., Koszel, M., & Shchur, T. (2022). Modelling wheat grain flow during sowing based on the model of grain with shifted center of gravity. Agricultural Engineeringthis link is disabled 26(1): 25–37.
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