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Stability Assessment of an Ore Mill Electric Drive Using Machine Learning

Marinka Baghdasaryan, Vardan Hovhannisyn

Abstract


The relevance of the study is due to the need to improve electric drive systems operated in harsh conditions. The goal of the study is to create a model for assessing the state of stability of the electric drive of an ore mill using machine learning capabilities, which will provide high performance and the ability to work consistently in different systems. Various sustainability assessment models have been developed based on 6 machine learning algorithms. The study and comparison of models built using artificial neural networks (ANN) of different architectures was carried out using various learning methods. The expediency of using the Tree and ANN algorithms to develop a model for assessing electric drive stability is substantiated. The novelty of the results obtained lies in the fact that the model has high accuracy, high speed, and the ability to detect instability in uncertain operating modes of the electric motor of an electric drive, as well as the possibility of coordinated operation with various systems. The practical value is that the model allows, at an intellectual level, to provide effective control and fault diagnosis of complex electric drive systems, which cannot be achieved using the known methods.

 

Doi: 10.28991/HIJ-2024-05-02-01

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Keywords


Machine Learning; Neural Network; Ore Mill; Electric Drive; Intelligent Model Discipline.

References


Zhou, Z., Hu, Y., Liu, B., Dai, K., & Zhang, Y. (2023). Development of Automatic Electric Drive Drilling System for Core Drilling. Applied Sciences (Switzerland), 13(2), 1059. doi:10.3390/app13021059.

Benbouzid, M. E. H., Diallo, D., & Zeraoulia, M. (2007). Advanced fault-tolerant control of induction-motor drives for EV/HEV traction applications: From conventional to modern and intelligent control techniques. IEEE Transactions on Vehicular Technology, 56(2), 519–528. doi:10.1109/TVT.2006.889579.

He, F., Xie, G., & Luo, J. (2020). Electrical bearing failures in electric vehicles. Friction, 8(1), 4–28. doi:10.1007/s40544-019-0356-5.

Zhang, Z., Schürhuber, R., Fickert, L., Friedl, K., Chen, G., & Zhang, Y. (2021). Systematic stability analysis, evaluation and testing process, and platform for grid-connected power electronic equipment. Elektrotechnik Und Informationstechnik, 138(1), 20–30. doi:10.1007/s00502-020-00857-y.

Baghdasaryan, M. K. (2019). Methods of research and optimization of the mineral raw material grinding process. In Methods of Research and Optimization of the Mineral Raw Material Grinding Process. NOVA Science Publishers, New York, United States.

Chernyi, S. G., Erofeev, P., Novak, B., & Emelianov, V. (2021). Investigation of the mechanical and electromechanical starting characteristics of an asynchronous electric drive of a two-piston marine compressor. Journal of Marine Science and Engineering, 9(2), 207. doi:10.3390/jmse9020207.

Ju, J., Liu, Y., & Zhang, C. (2022). Stability Analysis of Electromechanical Coupling Torsional Vibration for Wheel‐Side Direct‐Driven Transmission System under Transmission Clearance and Motor Excitation. World Electric Vehicle Journal, 13(3), 46. doi:10.3390/wevj13030046.

Kaniuk, G., Vasylets, T., Varfolomiyev, O., Mezerya, A., & Antonenko, N. (2019). Development of neural-network and fuzzy models of multimass electromechanical systems. Eastern-European Journal of Enterprise Technologies, 3(2–99), 51–63. doi:10.15587/1729-4061.2019.169080.

Ghosh, E. (2018). Machine Learning Based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application. Electronic Theses and Dissertations. Degree of Doctor of Philosophy, University of Windsor, Windsor, Ontario, Canada.

Baghdasaryan, M. K., & Davtyan, D. V. (2021). Energy saving in the ore beneficiation technological process by the optimization of reactive power produced by the synchronous motors. International Review of Electrical Engineering, 16(4), 377–384. doi:10.15866/iree.v16i4.20119.

Li, Z., Wang, Y., Li, K., Lin, W., & Tong, X. (2018). Study on the Performance of Ball Mill with Liner Structure based on DEM. Journal of Engineering and Technological Sciences, 50(2), 157–178. doi:10.5614/j.eng.technol.sci.2018.50.2.2.

Bai, Y., & He, F. (2015). Modeling on the effect of coal loads on kinetic energy of balls for ball mills. Energies, 8(7), 6859–6880. doi:10.3390/en8076859.

Góralczyk, M., Krot, P., Zimroz, R., & Ogonowski, S. (2020). Increasing energy efficiency and productivity of the comminution process in tumbling mills by indirect measurements of internal dynamics—an overview. Energies, 13(24), 6735. doi:10.3390/en13246735.

Le Roux, J. D., Olivier, L. E., Naidoo, M. A., Padhi, R., & Craig, I. K. (2016). Throughput and product quality control for a grinding mill circuit using non-linear MPC. Journal of Process Control, 42, 35–50. doi:10.1016/j.jprocont.2016.04.007.

Sapsalev, A. V., Kharitonov, S. A., & Achitaev, A. A. (2022). On the stability of two-mass electromechanical systems with magnetic couplings. Elektrotekhnika, 1(1), 2–6. doi:10.53891/00135860_2021_1_2.

Bortnowski, P., Gładysiewicz, L., Król, R., & Ozdoba, M. (2021). Energy efficiency analysis of copper ore ball mill drive systems. Energies, 14(6), 1786. doi:10.3390/en14061786.

Saldaña, M., Gálvez, E., Navarra, A., Toro, N., & Cisternas, L. A. (2023). Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry. Materials, 16(8), 3220. doi:10.3390/ma16083220.

Wu, S., Zhou, Y., Zhang, J., Ma, S., & Lian, Y. (2022). Research on Stability Control Method of Electro-Mechanical Actuator under the Influence of Lateral Force. Electronics (Switzerland), 11(8), 1237. doi:10.3390/electronics11081237.

Merabet, A. (2020). Advanced control for electric drives: Current challenges and future perspectives. Electronics (Switzerland), 9(11), 1–4. doi:10.3390/electronics9111762.

Blagodarov, D. A., Grigorian, D. D., & Safonov, Y. M. (2017). The Approach to Design Control System for the Electric Drive with Flexible Mechanics. Procedia Engineering, 206, 540–545. doi:10.1016/j.proeng.2017.10.513.

Gasiyarov, V. R., Radionov, A. A., Loginov, B. M., Zinchenko, M. A., Gasiyarova, O. A., Karandaev, A. S., & Khramshin, V. R. (2023). Method for Defining Parameters of Electromechanical System Model as Part of Digital Twin of Rolling Mill. Journal of Manufacturing and Materials Processing, 7(5), 183. doi:10.3390/jmmp7050183.

Kuznetsov, N. K., Dolgih, E. S., & Iov, I. A. (2019). Investigation of the Effectiveness of Oscillation Control in a Three-Mass System Based on the Additional Feedbacks. Advances in Engineering Research, 188, 211–216. doi:10.2991/aviaent-19.2019.40.

Nagy, B., Galata, D. L., Farkas, A., & Nagy, Z. K. (2022). Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing—a Review. AAPS Journal, 24(4), 74. doi:10.1208/s12248-022-00706-0.

Abraham, J., & Shrivastava, S. (2018). Dc Motor Speed Control Using Machine Learning Algorithm. International Journal of Engineering Research & Technology, 7(04), 456–470. doi:10.17577/IJERTV7IS040033.

Baghdasaryan, M. K., & Avetisyan, A. G. (2023). Study of Stability Conditions of the “Electric Drive Motor-Technological Mechanism” System. International Review of Electrical Engineering, 18(2), 111–118. doi:10.15866/iree.v18i2.22925.

He, R., & Han, Q. (2017). Dynamics and Stability of Permanent-Magnet Synchronous Motor. Mathematical Problems in Engineering, 4923987. doi:10.1155/2017/4923987.

Kodkin, V. L., Anikin, A. S., & Baldenkov, A. A. (2017). Analysis of Stability of Electric Drives as Non-linear Systems According to Popov Criterion Adjusted to Amplitude and Phase Frequency Characteristics of Its Elements. 2nd International Conference on Applied Mathematics, Simulation and Modelling (AMSM 2017), 7-14. doi:10.12783/dtetr/amsm2017/14810.

Kulakovskiy, Y. M., & Aristov, A. V. (2019). Dynamic Stability of the Valve Electric Drive in Oscillatory Mode. Journal of Physics: Conference Series, 1260(5), 052016. doi:10.1088/1742-6596/1260/5/052016.

Ibrahim, M. N., Rashad, E. M., & Sergeant, P. (2015). Transient analysis and stability limits for synchronous reluctance motors considering saturation effects. In 2015 18th International Conference on Electrical Machines and Systems, ICEMS 2015, 1812–1816. doi:10.1109/ICEMS.2015.7385335.

Pupin, V., & Orlov, V. (2023). Mathematical Calculation of Synchronous Electric Motors Dynamic Stability. Mathematics, 11(21), 4465. doi:10.3390/math11214465.

Toliyat, H. A., Nandi, S., Choi, S., & Meshgin-kelk, H. (2017). Electric machines: Modeling, condition monitoring, and fault diagnosis. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis. CRC Press, Florida, United States. doi:10.1201/b13008.

Gric, P. (2020). Protection against the Effects of the Asynchronous Operation of Synchronous Motors Based on the Principle of Comparison of the Machine Power Factor. Transactions on Electrical Engineering, 8(4), 63–67. doi:10.14311/tee.2019.4.063.

Baghdasaryan, M.K.; Babayan, A.H.; Avetisyan, A.G.; Hovhannisyan, V.D., (2023). Developing a Mathematical Model to Reveal and Evaluate the Conditions of Stability of the System “Electric Drive Synchronous Motor-Mechanism”. Proceedings National Polytechnic University of Armenia, Electrical Engineering, Energetics, 1, 38-49. doi:10.53297/18293328-2023.1-38.

Ivanchenko F.K., Krasnoshapka V.A. (1983). Dinamika Metallurgicheskikh Mashin. Metallurgy, Moscow, Russia, 199-205.

Hinton, G. (2017). Viualizing data using t-SNE Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(February), 2579–2605.

Feldesman, M. R. (2002). Classification trees as an alternative to linear discriminant analysis. American Journal of Physical Anthropology, 119(3), 257–275. doi:10.1002/ajpa.10102.

Hsieh, W. W. (2009). Machine learning methods in the environmental sciences: Neural networks and Kernels. In Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. University of British Columbia, Vancouver, Canada. doi:10.1017/CBO9780511627217.

Jinmeng, L. I., Lin, Y., & Zhu, T. k-Nearest Neighbor Classification Algorithm Based on Hubness and Class Weighting. Computer Engineering, 44, 248–252.

Li, Q. N., & Li, T. H. (2020). Research on the application of Naive Bayes and Support Vector Machine algorithm on exercises Classification. Journal of Physics: Conference Series, 1437(1), 012071. doi:10.1088/1742-6596/1437/1/012071.

Prabhat, A., & Khullar, V. (2017). Sentiment classification on big data using Naïve bayes and logistic regression. 2017 International Conference on Computer Communication and Informatics, ICCCI 2017, Coimbatore, India. doi:10.1109/ICCCI.2017.8117734.

Haykin, S. (2009). Neural Networks and Learning Machines. 3rd Ed. Prentice Hall, New York, United States.

Madhiarasan, M., & Louzazni, M. (2022). Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications. Journal of Electrical and Computer Engineering, 2022, 1–23. doi:10.1155/2022/5416722.

Suzuki, K. (2013). Artificial Neural Networks - Architectures and Applications. In Artificial Neural Networks - Architectures and Applications, 1-266. doi:10.5772/3409.

Tian, Y., Zhang, Y., & Zhang, H. (2023). Recent Advances in Stochastic Gradient Descent in Deep Learning. Mathematics, 11(3), 682. doi:10.3390/math11030682.

Abdulkadirov, R., Lyakhov, P., & Nagornov, N. (2023). Survey of Optimization Algorithms in Modern Neural Networks. Mathematics, 11(11), 2466. doi:10.3390/math11112466.


Full Text: PDF

DOI: 10.28991/HIJ-2024-05-02-01

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