10.14489/vkit.2024.10.pp.010-016

DOI: 10.14489/vkit.2024.10.pp.010-016

Джавадов Н. Г., Амиров А. М., Исмаилов В. М.
ТЕХНИЧЕСКИЕ АСПЕКТЫ СОЗДАНИЯ СИСТЕМЫ МОНИТОРИНГА МЕТАЛЛОРЕЖУЩЕГО ОБОРУДОВАНИЯ С ПРИМЕНЕНИЕМ МАШИННОГО ОБУЧЕНИЯ
(c. 10-16)

Аннотация. Рассмотрены особенности создания системы мониторинга металлорежущего оборудования с точки зрения сбора данных и обучения моделей. Показана экономическая целесообразность системы мониторинга технологических процессов с применением алгоритмов машинного обучения. Создание подобной системы позволяет снизить время простоя оборудования, снизить производственные затраты, а также повысить производительность предприятий. В результате анализа предыдущих работ в области умного производства сформулированы и описаны требования к датчикам для сбора данных – высокая чувствительность, быстрота отклика, точность, обеспечение высокого отношения параметра сигнал/шум, многофункциональность, устойчивость к производственной среде, низкая стоимость, возможность быстрого развертывания системы и модульность. Рассмотрены и описаны основные параметры данных, которые необходимо учитывать при выборе и обучении моделей, в том числе нелинейность, негауссовость, шумность, высокая размерность, мультикластерность и динамичность. Приведены примеры моделирования поведения неисправных переменных технологических процессов.

Ключевые слова: металлообработка; умное производство; машинное обучение; индустрия 4.0; предиктивное обслуживание; датчики; обработка больших данных.

Javadov N. G., Amirov A. M., Ismayilov V. M.
TECHNICAL ASPECTS RELATED TO THE DEVELOPMENT OF A MONITORING SYSTEM FOR METAL-CUTTING EQUIPMENT USING MACHINE LEARNING
(pp. 10-16)

Abstract. The purpose of this article is to examine the features of developing a monitoring system for metal-cutting machines from the point of view of data collection and model training. This paper examined the economic feasibility of a process monitoring system with machine learning based algorithms. The development of such a monitoring system can reduce equipment downtime, reduce production costs, and increase the productivity of plants. As a result of analysing previous work in the field of smart manufacturing, the requirements for data acquisition sensors were formulated and described – high sensitivity, fast response, accuracy, providing high signal-to-noise ratio, versatility, robustness to the manufacturing environment, low cost, rapid system deployment and modularity. The paper reviewed and described the main data parameters that need to be considered in model selection and training, including nonlinearity, non-Gaussianity, noisiness, high dimensionality, multiclustering and dynamism. The results of modelling the behaviour of faulty process variables were visually demonstrated. The modelling was carried out in order to show that the process variables are not fixed values, therefore, it is not possible to directly compare the process variable with some fixed threshold value, i.e. it is necessary to take into consideration the dynamics of changes in the variable even in the threshold values of normal behaviour. The machine learning models complexity in terms of computing power requirements was also discussed.

Keywords: Metalworking; Smart manufacturing; Machine learning; Industry 4.0; Predictive maintenance; Sensors; Big data processing.

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Н. Г. Джавадов (Национальное аэрокосмическое агентство, Баку, Азербайджанская Республика)
А. М. Амиров, В. М. Исмаилов (Научно-исследовательский институт аэрокосмической информатики Национального аэрокосмического агентства, Баку, Азербайджанская Республика) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

Eng

N. G. Javadov (National Aerospace Agency, Baku, Azerbaijan Republic)
A. M. Amirov, V. M. Ismayilov (Research Institute of Aerospace Informatics of the National Aerospace Agency, Baku, Azerbaijan Republic) E-mail: Этот e-mail адрес защищен от спам-ботов, для его просмотра у Вас должен быть включен Javascript

+ - Библиографический список (References) Click to collapse

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1. Kunpeng Z. Smart Machining System. Modeling, Monitoring and Informatics / Springer. Series in Advanced Manufacturing. Cham, Switzerland: Springer, 2022. 420 p.
2. Ramesh K., Arbind P., Ashwani K. Sustainable Smart Manufacturing Processes in Industry 4.0. Dehradun, India: CRC Press, 2023. 283 p.
3. Kim P. T. Artificial Intelligence for Smart Manufacturing Methods, Applications, and Challenges. Cham, Switzerland: Springer, 2023. 271 p.
4. Grzesik W. Advanced Machining Processes of Metallic Materials. Theory, Modelling, and Applications. 2nd Ed. Amsterdam: Elsevier, 2017. 592 p.
5. Fahle S., Prinz C., Kuhlenkötter B. Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application // Procedia CIRP. 2020. V. 93. P. 413–418.
6. Bequette B. W. Process Control: Modeling, Design, and Simulation. London, Great Britan: Pearson, 2003. 800 p.
7. Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data / W. J. Lee, H. Wu, H. Yun et al. // 26th CIRP Conference on Life Cycle Engineering (LCE) Purdue University. West Lafayette, IN, USA. 7–9 May, 2019. P. 506–511.
8. A Multi-Sensor Based Online Tool Condition Monitoring System for Milling Process / X. Y. Zhang, X. Lu, S. Wang et al. // 51st CIRP Conference on Manufacturing Systems. 2018. V. 72. P. 1136–1141.
9. Zhang C., Zhang J. On-Line Tool Wear Measurement for Ball-End Milling Cutter Based on Ma-chine Vision // Computers in Industry. 2013. V. 64, Is. 6. P. 708–719.
10. Coleman C., Damofaran S., Deuel E. Predictive Maintenance and the Smart Factory. New York, USA: Deloitte Consulting LLP, 2017. 8 p.
11. A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment / J. M. Failing, J. V. Abellán-Nebot, S. Benavent Nácher et al. // Processes. 2023. V. 11. P. 668. 18 p.
12. Wang L., Gao R. X. Condition Monitoring and Control for Intelligent Manufacturing // Springer Series in Advanced Manufacturing. Springer – Verlag, London Limited, 2006. 410 p.
13. Hassan I. U., Panduru K., Walsh J. An In-Depth Study of Vibration Sensors for Condition Monitor-ing // Sensors. 2024. V. 24(3). P. 740.
14. A Review of IoT Sensing Applications and Challenges Using RFID and Wireless Sensor Networks / H. Landaluce, L. Arjona, A. Perallos et al. // Sensors. 2020. V. 20. P. 2495. 18 p.
15. Sarker I. H. Machine Learning: Algorithms, Real-World Applications and Research Directions // SN Computer Science. 2021. V. 2. P. 160. 21 p.
16. Kumar A., Flores-Cerrillo J. Machine Learning in Python for Process and Equipment Condition Mon-itoring, and Predictive Maintenance. ML for PSE, 2024. 361 p.
17. Kumar A., Flores-Cerrillo J. Machine Learning in Python for Process Systems Engineering. ML for PSE, 2022. 352 p.
18. Wu Z., Rincon D., Luo J., Christofides P. D. Machine Learning Modeling and Predictive Control of Nonlinear Processes Using Noisy Data // AIChE Journal. 2021. V. 67, Is. 4. 15 p.
19. Wu X., Brazzle P., Cahoon S. Performance and Energy Consumption of Parallel Machine Learning Algorithms // arXiv: 2305.00798. htts://doi:org/10.48550/arXiv2305.00798

Eng

1. Kunpeng Z. (2022). Smart Machining System. Modeling, Monitoring and Informatics. Springer. Series in Advanced Manufacturing. Cham: Springer.
2. Ramesh K., Arbind P., Ashwani K. (2023). Sustainable Smart Manufacturing Processes in Industry 4.0. Dehradun: CRC Press.
3. Kim P. T. (2023). Artificial Intelligence for Smart Manufacturing Methods, Applications, and Chal-lenges. Cham: Springer.
4. Grzesik W. (2017). Advanced Machining Pro-cesses of Metallic Materials. Theory, Modelling, and Applications. 2nd ed. Amsterdam: Elsevier.
5. Fahle S., Prinz C., Kuhlenkötter B. (2020). Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application. Procedia CIRP, 93, 413 – 418.
6. Bequette B. W. (2003). Process Control: Modeling, Design, and Simulation. London: Pearson.
7. Lee W. J., Wu H., Yun H. et al. (2019). Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data. 26th CIRP Conference on Life Cycle Engineering (LCE) Purdue University. West Lafayette.
8. Zhang X. Y., Lu X., Wang S. et al. (2018). A Multi-Sensor Based Online Tool Condition Monitoring System for Milling Process. 51st CIRP Conference on Manufacturing Systems, 72, 1136 – 1141.
9. Zhang C., Zhang J. (2013). On-Line Tool Wear Measurement for Ball-End Milling Cutter Based on Machine Vision. Computers in Industry, 64(6), 708 – 719.
10. Coleman C., Damofaran S., Deuel E. (2017). Predictive Maintenance and the Smart Factory. New York: Deloitte Consulting LLP.
11. Failing J. M., Abellán-Nebot J. V., Benavent Nácher S. et al. (2023). A Tool Condition Monitoring System Based on Low-Cost Sensors and an IoT Platform for Rapid Deployment. Processes, 11.
12. Wang L., Gao R. X. (2006). Condition Monitoring and Control for Intelligent Manufacturing. Springer Series in Advanced Manufacturing. Springer – Verlag, London Limited.
13. Hassan I. U., Panduru K., Walsh J. (2024). An In-Depth Study of Vibration Sensors for Condition Moni-toring. Sensors, 24(3).
14. Landaluce H., Arjona L., Perallos A. et al. (2020). A Review of IoT Sensing Applications and Chal-lenges Using RFID and Wireless Sensor Networks. Sen-sors, 20.
15. Sarker I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2.
16. Kumar A., Flores-Cerrillo J. (2024). Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance. ML for PSE.
17. Kumar A., Flores-Cerrillo J. (2022). Machine Learning in Python for Process Systems Engineering. ML for PSE.
18. Wu Z., Rincon D., Luo J., Christofides P. D. (2021). Machine Learning Modeling and Predictive Control of Nonlinear Processes Using Noisy Data. AIChE Journal, 67(4).
19. Wu X., Brazzle P., Cahoon S. Performance and Energy Consumption of Parallel Machine Learning Algorithms. arXiv: 2305.00798. Retrieved from htts://doi:org/10.48550/arXiv2305.00798

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