Analysis of a Dataset for Modeling a Transport Conveyor

Pihnastyi, Oleh and Burduk, Anna (2022): Analysis of a Dataset for Modeling a Transport Conveyor. Published in: CEUR Workshop Proceedings , Vol. 3309, (26 November 2022): pp. 319-328.

Abstract

The analysis of the works, which considered the use of neural networks for modeling a multi-section transport conveyor, was carried out. The prospects for the use of neural networks for the design of highly efficient control systems for the flow parameters of a multi-section transport conveyor are studied. The problem that limits the use of neural networks for building control systems for the flow parameters of a multi-section transport conveyor is considered. The possibility of constructing generators for generating a data set for the process of training a neural network is being studied. A method for generating a data set based on experimentally obtained measurements of the instantaneous values of the input material flow as a result of the operation of industrial transport systems is proposed. Using dimensionless variables, a statistical analysis of a stochastic flow of material entering the input of the transport system was performed. An estimate of the correlation time of a stochastic process characterizing the input flow of material is given. The recommendations on choosing the type of correlation function for the model of the input material flow were confirmed. It is demonstrated that the input flow of material is a non-stationary stochastic process. Approximations for modeling the input flow of materials of the operating transport system are considered.

• Item Type: MPRA Paper
• Language: English
• Keywords: transport conveyor; neural network; non-stationary stochastic process; dataset generator
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory; D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity

References

1. M. Alspaugh, "Latest developments in belt conveyor technology," MINExpo 2004, New York, USA, 2004.

2. M. Alspauch, "The evolution of intermediate driven belt conveyor," Bulk Solids Handling, vol. 23(3), pp. 168–173, 2003. http://www.overlandconveyor.com/pdf/bsh_AlspaughM_3_2003.pdf

3. I. Halepoto, M Shaikh, and B. Chowdhry, "Design and Implementation of Intelligent Energy Efficient Conveyor System Model Based on Variable Speed Drive Control and Physical Modeling, " Journal of Control and Automation, vol. 9(6), pp. 379–388, 2016.

4. O. Pihnastyi, V. Khodusov, "Optimal Control Problem for a Conveyor-Type Production Line," Cybern. Syst. Anal., vol. 54(5), pp. 744–753, 2018. https://doi.org/10.1007/s10559-018-0076-2

5. O. Pihnastyi, V. Khodusov, "The optimal control problem for output material flow on conveyor belt with input accumulating bunker," Bulletin of the South Ural State University. Ser.Mathematical Modelling, Programming & Computer Software, vol. 12(2), pp. 67–81, 2019. http://dx.doi.org/10.14529/mmp190206

6. P. Bardzinski, P. Walker, and W. Kawalec, "Simulation of random tagged ore flow through the bunker in a belt conveying system," International Journal of Simulation Modelling, vol. 4, pp. 597– 608 (2018). https://doi.org/10.2507/IJSIMM17(4)445

7. T. Mathaba, X. Xia, and J. Zhang, "Optimal scheduling of conveyor belt systems under Critical Peak Pricing," 10th International Power & Energy Conference (IPEC), pp. 315–320, 2012. https://doi.org/10.1109/ASSCC.2012.6523285

8. T. Cousins, "Using Time of Use (TOU) Tariffs in Industrial, Commercial and Residential Applications Effectively," TLC Engineering Solutions, pp. 1–15, 2010

9. A. Kirjanow, "The possibility for adopting an artificial neural network model in the diagnostics of conveyor belt splices," Interdisciplinary issues in mining and geology, vol. 6, , pp. 1–11, 2016

10. Xi. Pingyuan, S. Yandong, "Application Research on BP Neural Network PID Control of the Belt Conveyor," JDIM, vol. 9(6), pp. 266–270, 2011

11. I. Shareef, H. Hussein, "Implementation of artificial neural network to achieve speed Control and Power Saving of a Belt Conveyor System," Eastern-European Journal of enterprise technologies, vol. 2(110), pp. 44–53, 2021. https://doi.org/10.15587/1729-4061.2021.224137

12. Y. Yuan, W. Meng, and X. Sun, "Research of fault diagnosis of belt conveyor based on fuzzy neural network," The Open Mechanical Engineering Journal, vol. 8, pp. 916–921, 2014. https://doi.org/10.2174/1874155X01408010916

13. S. Abdollahpor, A. Mahmoudi, and A. Mirzazadeh, "Artificial neural network prediction model for material threshing in combine harvester," Elixir Agriculture, vol. 52, pp. 11621–1162, 2012. https://www.elixirpublishers.com/articles/1353477517_52%20(2012)%2011621-11626.pdf

14. O. Pihnastyi, G. Kozhevnikov, "Control of a Conveyor Based on a Neural Network," International Conference on Problems of Infocommunications. Science and Technology, pp. 295– 300, 2020. https://doi.org/10.1109/PICST51311.2020.9467934

15. M. Koman, Z. Laska, "The constructional solution of conveyor system for reverse and bifurcation of the ore flow, Rudna mine KGHM Polska Miedź SA," CUPRUM, vol. 3(72), pp. 69– 82, 2014

16. M. Bajda, R. Błażej, and L. Jurdziak, "Analysis of changes in the length of belt sections and the number of splices in the belt loops on conveyors in an underground mine," Engineering Failure Analysis, vol. 101, pp. 439–446, 2019. https://doi.org/10.1016/j.engfailanal.2019.04.003

17. R. Król, W. Kawalec, L. Gładysiewicz, "An effective belt conveyor for underground ore transportation systems," Earth and Environmental Science, vol. 95(4), pp. 1–9, 2017. https://doi.org/10.1088%2F1755-1315%2F95%2F4%2F042047

18. V. Prokuda, "Research and assessment of cargo flows on the main conveyor transport "PSP Pavlogradskaya Mine, DTEK Pavlogradugol," Mining electromechanics, vol. 288, pp. 107–111, 2012

19. V. Zaika, Yu. Razumny, V. Prokuda, "Regulated drives influence on coal flow and energy efficiency of mine conveyor transport system," Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, vol. 3, pp. 82–88, 2015

20. V. Kondrakhin, N. Stadnik, P. Belitsky, "Statistical Analysis of Mine Belt Conveyor Operating Parameters," Naukovi pratsi DonNTU, vol. 2(26), pp. 140–150, 2013

21. O. Pihnastyi, V. Khodusov, "Model of a composite magistral conveyor line," IEEE International Conference on System analysis & Intelligent computing, Ukraine, pp. 68–72, 2018. https://doi.org/10.1109/saic.2018.8516739

22. O. Pihnastyi, V. Khodusov, "Calculation of the parameters of the composite conveyor line with a constant speed of movement of subjects of labour," Scientific bulletin of National Mining University, vol. 4(166), pp. 138–146, 2018 –https://doi.org/10.29202/nvngu/2018-4/18

23. O. Pihnastyi, G. Kozhevnikov and T. Bondarenko, "An Analytical Method for Generating a Data Set for a Neural Model of a Conveyor Line,"  IEEE 11th International Conference on Dependable Systems, Services and Technologies, Ukraine, pp. 202–206, 2020. https://doi.org/10.1109/DESSERT50317.2020.9125041

24. B. Jeftenić, L. Ristić, M. Bebić, S. Štatkić, D. Jevtić, I. Mihailović, and N. Rašić, "Realization of system of belt conveyors operation with remote control," Structural integrity and life, vol. 10(1), pp. 21–30, 2010. http://divk.inovacionicentar.rs/ivk/ivk10/021-030-IVK1-2010-BJ-LR-MB-SS- DJ-IM-NR.pdf

25. B. Doroszuk, R. Król, and J. Wajs, "Simple design solution for harsh operating conditions: redesign of conveyor transfer station with reverse engineering and DEM simulations," Energies, vol. 14, pp. 1–13, 2021. https://doi.org/10.3390/en14134008

26. F. Zeng, C. Yan, Q. Wu, T. Wang, "Dynamic behaviour of a conveyor belt considering non- uniform bulk material distribution for speed control," Applied sciences, vol. 10, pp. 1–13, 2020. https://doi.org/10.3390/app10134436

27. N. Stadnik, "Frequency-Controlled Electric Drive of Band Conveyors Based on Self- Ventilating Engines," Scientific Bulletin of the Donetsk National Technical University, vol. 2, pp. 226–232, 2012

28. A. Semenchenko, M. Stadnik, P. Belitsky, D. Semenchenko, and O. Stepanenko, "The impact of an uneven loading of a belt conveyor on the loading of drive motors and energy consumption in transportation," Eastern-European Journal of Enterprise Technologies, vol. 82, pp. 42–51, 2016. https://doi.org/10.15587/17294061.2016.75936

29. W. Kawalec, R. Król, "Generating of Electric Energy by a Declined Overburden Conveyor in a Continuous Surface Mine," Energies, vol. 14, pp. 1–13, 2021. https://doi.org/10.3390/en14134030

30. V. Kondrakhin, N. Stadnik, P. Belitsky, "Traffic measurement on the belt conveyor through removable tensor device with belt tension accounting," DonNTU scientific works, vol. 1, pp. 79– 87, 2013

31. L. Gladysiewicz, W. Kawalec, "Optimised Selection Of A Belt Conveyor Loaded By A BWE," 8th InternationalSymposium of Continuous Surface Mining ISCSM, Aachen, Germany, pp. 24–27, September 2006

32. A. Tynynyka, "Application of the entropic coefficient for interval number optimization during interval assessment," Technology and design in electronic equipment, vol. 3, pp. 49–54, 2017. https://www.readcube.com/articles/10.15222%2Ftkea2017.3.49

33. I. Heinhold, K. Gaede, Ingeniur statistic.Springler Verlag, 1964.Wang, Xin, Tapani Ahonen, and Jari Nurmi. "Applying CDMA technique to network-on-chip." IEEE transactions on very large scale integration (VLSI) systems 15.10 (2007): 1091-1100