Main Article Content

Oleh Pihnastyi
Georgii Kozhevnikov
Anna Burduk


The object of research is a stochastic input flow of material coming in the input of a conveyor-type transport system. Subject of research is the development of a method for generating values of the stochastic input material flow of transport conveyor to form a training data set for neural network models of the transport conveyor. The goal of the research is to develop a method for generating random values to construct implementations of the input material flow of a transport conveyor that have specified statistical characteristics calculated based on the results of previously performed experimental measurements. The article proposes a method for generating a data set for training a neural network for a model of a branched, extended transport conveyor. A method has been developed for constructing implementations of the stochastic input flow of material of a transport conveyor. Dimensionless parameters are introduced to determine similarity criteria for input material flows. The stochastic input material flow is presented as a series expansion in coordinate functions. To form statistical characteristics, a material flow implementation based on the results of experimental measurements is used. As a zero approximation for expansion coefficients, that are random variables, the normal distribution law of a random variable is used. Conclusion. It is shown that with an increase in the time interval for the implementation of the input material flow, the correlation function of the generated implementation steadily tends to the theoretically determined correlation function. The length of the time interval for the generated implementation of the input material flow was estimated.

Article Details

How to Cite
Pihnastyi , O. ., Kozhevnikov , G. ., & Burduk , A. . (2024). METHOD FOR GENERATING A DATA SET FOR TRAINING A NEURAL NETWORK IN A TRANSPORT CONVEYOR MODEL. Advanced Information Systems, 8(2), 79–88.
Intelligent information systems
Author Biographies

Oleh Pihnastyi , National Technical University "Kharkiv Polytechnic Institute", Kharkiv

Doctor of Technical Sciences, Professor, Professor of Multimedia and Internet Technologies and Systems Department

Georgii Kozhevnikov , National Technical University "Kharkiv Polytechnic Institute", Kharkiv

Candidate of Technical Sciences, Associate Professor, Professor of Multimedia and Internet Technologies and Systems Department

Anna Burduk , University of Science and Technology, Wrocław

Doctor Habilitation Inżynier, Doctor of Technical Sciences, Professor of Laser Technologies, Automation and Production Organization Department


Tintaya, H. (2002), DIN 22101:2002-08. Continous conveyors. Belt conveyors for loose bulk materials. Basics for calculation and dimensioning. GERMAN STANDARD, Normenausschuss Maschinenbau (NAM) im DIN, 51 p., available at:

SIEMENS (2023), “SIMINE portfolio for mining transportation”, Siemens Xcelerator Marketplace, available at:

Alspaugh, M. (2004), “Latest developments in belt conveyor technology”, 2004 Int. Conf. MINExpo, Las Vegas, USA,

pp. 1-5,

Mathaba, T. and Xia, X. (2015), “A parametric energy model for energy management of long belt conveyors”, Energies, vol. 12, no. 8, pp. 13590–13608, doi:

Stadnik, M., Semenchenko, D., Semenchenko, A., Belytsky, P., Virych, S. and Tkachov, V. (2019), “Improving energy efficiency of coal transportation by adjusting the speeds of a combine and a mine face conveyor”, Eastern-European Journal of Enterprise Technologies, vol. 1, no. 8(97), pp. 60–70, doi:

Pihnastyi, O. and Khodusov, V. (2020), “Development of the controlling speed algorithm of the conveyor belt based on TOU-tariffs”, 2020 Int. Conf. Information-Communication Technologies & Embedded Systems (ICT&ES), Mykolaiv, Ukraine, pp. 1–14, available at:

Semenchenko, A., Stadnik, M., Belitsky, P., Semenchenko, D. and Stepanenko, O. (2016), “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. 4, no. 1(82), pp. 42–51, doi:­4061.2016.75936

Pihnastyi, O., Khodusov, V. and Kotova, A. (2022), “The problem of combined optimal load flow control of main conveyor line“, Acta Montanistica Slovaca, vol. 27, no. 1, pp. 216–229, doi:

Ristić, L., Bebić M. and Jeftenić, B. (2013), “Development of the Algorithm for Energy Efficiency Improvement of Bulk Material Transport System“, Electronics, no. 8, pp. 30–39, doi:

Halepoto, I., Shaikh, M. and Chowdhry B. (2016), “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. 6, no. 9, pp. 379–388, doi:

Bardzinsk, P., Walker, P. and Kawalec, W. (2018), “Simulation of random tagged ore flow through the bunker in a belt conveying system“, International Journal of Simulation Modelling, no. 4, pp. 597–608, doi:

Kawalec, W. and Król, R. (2016), “Generating of Electric Energy by a Declined Overburden Conveyor in a Continuous Surface Mine”, Energies, vol. 13, no.14, doi:

Pihnastyi, O. and Chernіavska, S. (2022), “Improvement of methods for description of a three-bunker collection conveyor”, Eastern-European Journal of Enterprise Technologies, vol. 4, no.5(119), pp.33–41, doi:

Pihnastyi, O, Kozhevnikov, G. and Polozhyi, D. (2023), “Synthesis of an Optimal Control Algorithm for a Transport Conveyor with a Reversing Section", 2023 Int. Conf. IEEE 4th KhPI Week on Advanced Technology, Kharkiv, Ukraine, pp. 187–191, doi:

Marais, J. (2007), “Analysing DSM opportunities on mine conveyor systems”, Thesis (Electronical Engineering) -

North-West University, Potchefstroom Campus, Cape Town, South Africa, 145, p., available at:

He, D., Pang, Y., Lodewijks, G. and Liu, X. (2016), “Determination of Acceleration for Belt Conveyor Speed Control in Transient Operation”, International Journal of Engineering and Technology, vol. 3, no. 8, pp. 206–211, doi:

Zhang, S. and Xia, X. (2010), “Optimal control of operation efficiency of belt conveyor systems”, Applied Energy, vol. 87, no 6, pp. 1929–1937, doi:

Pihnastyi, O. and Khodusov, V. (2020), “Hydrodynamic Model of Transport System”, East European Journal of Physics, no. 1, pp. 121–136, doi:

Andrejiova, M. and Marasova, D. (2013), “Using the classical linear regression model in analysis of the dependences of conveyor belt life”, Acta Montanistica Slovaca, Technical University of Kosice, No. 18(2), pp. 77–84, available at:

Lu, Ya. and Li, Q. (2019), “A regression model for prediction of idler rotational resistance on belt conveyor”, Measurement and Control, No. 52(5), pp. 441–448, doi:

Wiecek, D., Burduk, A. and Kuric, I. (2019), “The use of ANN in improving efficiency and ensuring the stability of the copper ore mining process”, Acta Montanistica Slovaca, vol. 24, no 1, pp. 1–14, available at:

Xinglei, L. and Hongbin, Y. (2015), “The Design and Application of Control System Based on the BP Neural Network”, III International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS), Tianjin, China, pp. 789–793, available at:

Shareef, I.R. and Hussein, H.K. (2021), “Implementation of Artificial Neural Network to Achieve Speed Control and Power Saving of a Belt Conveyor System”, Eastern-European Journal of Enterprise Technologies, no. 2(2 (110)), pp. 44–53, doi:

Pihnastyi, O. and Khodusov, V. (2020), “Neural model of conveyor type transport system”, Third International Workshop on Computer Modeling and Intelligent Systems, CEUR Workshop Proceedings, vol. 2608, pp. 804–818, available at:

Król, R., Kawalec, W. and Gładysiewicz, L. (2017), “An effective belt conveyor for underground ore transportation systems”, IOP Conference Series: Earth and Environmental Science, vol. 95, no. 4, pp. 1–9, doi:

Bajda, M., Błażej, R. and Jurdziak, L. (2019), “Analysis of changes in the length of belt sections and the number of splices in the belt loops on conveyors in an Alspaugh underground mine”, Engineering Failure Analysis, no. 101, pp. 439–446. doi:

Pihnastyi, O., Khodusov, V. and Kotova, A. (2023), “Mathematical model of a long-distance conveyor”, Mining Science, no. 30, pp. 27–43, doi:

Pihnastyi, O. and Ivanovska, O. (2021), “Improving the Prediction Quality for a Multi-Section Transport Conveyor Model Based on a Neural Network”, Int. conf. Information Technology and Implementation (IT&I-2021), Kyiv, Ukraine, vol. 3132, pp. 24–38, available at:

Bhadani, K., Asbjörnsson, G., Hulthén, E., Hofling, K. and Evertsson, M. (2021), “Application of Optimization Method for Calibration and Maintenance of Power-Based Belt Scale”, Minerals, vol. 11, no 4, doi:

Zeng, F., Yan, C., Wu, Q. and Wang, T. (2020), “Dynamic behaviour of a conveyor belt considering non-uniform bulk material distribution for speed control”, Applied Sciences, no. 10(13), pp. 1–19, doi:

Curtis, A. and Sarc, R. (2021), “Real-time monitoring volume flow, mass flow and shredder power consumption in mixed solid waste processing”, Waste Management, no. 131, pp. 41–49, doi:

Carvalho, R. and Nascimento, Garcia L. (2020), “A UAV-based framework for semi-automated thermographic inspection of belt conveyors in the mining industry”, Sensors, no. 22(8), pp. 1–19, doi: