Decomposition of failure states of grain harvesting combiners
DOI:
https://doi.org/10.31548/dopovidi.2(108).2024.024Keywords:
reliability, decomposition, simulation, model, harvesting combineAbstract
The article discloses methodological approaches to the formation of the validity of the application of the simulation model of the decomposition of failure-free states of combine harvesters. The authors reproduce the functional decomposition of the reliability of grain harvesters obtained through the analysis of system functions. The authors reveal analytical approaches to the question of what the system does, regardless of how it works. As a basis, the authors formed a division into functional subsystems in the community of functions performed by groups of elements. When conducting the experiment, a set of random events that occur with the module under consideration is first generated - the event of failure of the blocked module, the event of activation of the blocking of the module and the event of the transmission of the module. Then, by assigning the corresponding probabilities, states are defined and logical dependencies between them are found. The experiment consisted in the sequential generation of events and obtaining the final states of the functional module. This experiment was repeated several times, then it was calculated, in several cases the functional module of the grain harvester was in good condition. The ratio of the number of experiment results in which the functional module was found to be working to all results shows the possibility of the functioning of the functional module. By performing a certain number of calculations of each state, summing the results of each calculation, taking the true value as one and the false value as zero, and then dividing the result by the length of the set, we obtain the value of the probability of blocked failure calculated by the Monte Carlo method.When performing the calculations and with the original data, it was equal to 0.885. The similarity of the result to the result accepted as true proves the truth. The method given in the article allows to improve the quality of work of the fail-safe system of grain harvesters, providing more accurate failure-free accounting, expanding the list of analyzed reliability parameters and making fuller use of technical means of monitoring technical condition parameters.References
Parekh, D., Poddar, N., Rajpurkar, A., Chahal, M., Kumar, N., Joshi, G. P. & Cho, W. (2022). A review on autonomous vehicles: progress, methods and challenges. Electronics, 11 (14), 2162. https://doi.org/10.3390/electronics11142162.
Aulin, V., Rogovskii, I., Lyashuk, O., Titova, L., Hrynkiv, A., Mironov, D., Volianskyi, M., Rogatynskyi, R., Solomka, O. & Lysenko, S. (2024). Comprehensive assessment of technical condition of vehicles during operation based on Harrington’s desirability function. Eastern-European Journal of Enterprise Technologies, 1 (3(127)), 37–46. https://doi.org/10.15587/1729-4061.2024.298567.
Prytz, R., Nowaczyk, S., Rögnvaldsson, T. & Byttner, S. (2015). Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering Applications of Artificial Intelligence, 41, 139–150. https://doi.org/10.1016/j.engappai.2015.02.009.
Volokha, M., Rogovskii, I., Fryshev, S., Sobczuk, H., Virchenko, G. & Yablonskyi, P. (2023). Modeling of transportation process in a technological complex of beet harvesting machines. Journal of Engineering Sciences (Ukraine), 10(2), F1-F9, https://doi.org/10.21272/jes.2023.10(2).f1.
Chen, X., Chen, J. (2020). Optimization of the impeller geometry for an automotive torque converter using response surface methodology and desirability function. Open Journal of Applied Sciences, 10 (07), 455–475. https://doi.org/10.4236/ojapps.2020.107032.
Rogovskii, I., Lyubarets, B. & Borek, K. (2022). Analyticity of non-stationary processes of change in diagnostic parameters of hydrostatic transmissions of harvesters. Machinery and Energetics, 13(1), 67–76. https://doi.org/10.31548/machenergy2022.01.067.
Ahmed, E., Gharavi, H. (2018). Cooperative vehicular networking: a survey. IEEE Transactions on Intelligent Transportation Systems, 19 (3), 996–1014. https://doi.org/10.1109/tits.2018.2795381.
Rogovskii, I.L. (2021). Models of formation of engineering management alternatives in methods of increasing grain production in agricultural enterprises. Machinery and Energetics, 12(1), 137–146, https://doi.org/10.31548/machenergy2021.01.137.
Padilla-Atondo, J. M., Limon-Romero, J., Perez-Sanchez, A., Tlapa, D., Baez-Lopez, Y., Puente, C. & Ontiveros, S. (2021). The impact of hydrogen on a stationary gasoline-based engine through multi-response optimization: a desirability function approach. Sustainability, 13 (3), 1385. https://doi.org/10.3390/su13031385.
Rolison, J., Regev, S., Moutari, S. & Feeney, A. (2018). What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records. Accident Analysis & Prevention, 115, 11–24. https://doi.org/10.1016/j.aap.2018.02.025.
Rogovskii, I. L. (2021). Resource of removal expenses for strong agricultural period of volume of operations. Machinery and Energetics, 12(2), 123–131. https://doi.org/10.31548/machenergy2021.02.123.
Nazarenko, I., Bernyk, I., Dedov, O., Rogovskii, I., Ruchynskyi, M., Pereginets, I., & Titova, L. (2021). Research of technical systems of processes of mixing materials. Dynamic processes in technological technical systems. Kharkiv: РС Тесhnology Сеntеr. 57-76. https://doi.org/10.15587/978-617-7319-49-7.ch4.
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, https://doi.org/10.1088/1755-1315/677/5/052002.
Gorea, R. K. (2016). Financial impact of road traffic accidents on the society. International Journal of Ethics, Trauma & Victimology, 2 (01), 6–9. https://doi.org/10.18099/ijetv.v2i1.11129
Rogovskii, I.L. (2021). Influence of operating failure of agricultural machines on efficiency of their machine use. Machinery and Energetics, 12(3), 157–166. https://doi.org/10.31548/machenergy2021.03.157
Rogovskii, I.L. (2021). Analyticity of complex criteria for evaluation of grain production in agricultural enterprises intensification of engineering management. Machinery and Energetics, 12(4), 129–138. https://doi.org/10.31548/machenergy2021.04.129
Britton, M., Asnaashari, S. & Read, G. (2016). Analysis of train derailment cause and outcome in Victoria, Australia, between 2007 and 2013: implications for regulation. Journal of Transportation Safety & Security, 9 (1), 45–63. https://doi.org/10.1080/19439962.2015.1088906.
Dirnbach, I., Kubjatko, T., Kolla, E., Ondruš, J. & Šarić, Ž. (2020). Methodology Designed to Evaluate Accidents at Intersection Crossings with Respect to Forensic Purposes and Transport Sustainability. Sustainability, 12 (5), 1972. https://doi.org/10.3390/su12051972.
Dela Cruz, O.G., Padilla, J.A. & Victoria, A.N. (2021). Managing Road Traffic Accidents: A Review on Its Contributing Factors. IOP Conference Series: Earth and Environmental Science, 822 (1), 012015. https://doi.org/10.1088/1755-1315/822/1/012015.
Low, R., Tekler, Z. & Cheah, L. (2020). Predicting commercial vehicle parking duration using generative adversarial multiple imputation networks. transportation research record. Journal of the Transportation Research Board, 2674(9), 820–831. https://doi.org/10.1177/0361198120932166.
Young, W., Sobhani, A., Lenné, M. & Sarvi, M. (2014). Simulation of safety: A review of the state of the art in road safety simulation modelling. Accident Analysis & Prevention, 66, 89–103. https://doi.org/10.1016/j.aap.2014.01.008.
Slobodyanyuk, M., Gorobchenko, O. (2020). Structural analysis of territorial transport systems based on classification methods. Eastern-European Journal of Enterprise Technologies, 1 (4(103)), 23–32. https://doi.org/10.15587/1729-4061.2020.194158.
Oladimeji, D., Gupta, K., Kose, N., Gundogan, K., Ge, L. & Liang, F. (2023). Smart transportation: an overview of technologies and applications. Sensors, 23 (8), 3880. https://doi.org/10.3390/s23083880.
Casado-Sanz, N., Guirao, B. & Attard, M. (2020). Analysis of the risk factors affecting the severity of traffic accidents on spanish crosstown roads: the driver’s perspective. Sustainability, 12 (6), 2237. https://doi.org/10.3390/su12062237.
Abu Dabous, S., Ibrahim, F., Feroz, S. & Alsyouf, I. (2021). Integration of failure mode, effects, and criticality analysis with multi-criteria decision-making in engineering applications: Part I – Manufacturing industry. Engineering Failure Analysis, 122, 105264. https://doi.org/10.1016/j.engfailanal.2021.105264.
Downloads
Published
Issue
Section
License
Relationship between right holders and users shall be governed by the terms of the license Creative Commons Attribution – non-commercial – Distribution On Same Conditions 4.0 international (CC BY-NC-SA 4.0):https://creativecommons.org/licenses/by-nc-sa/4.0/deed.uk
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
