kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2024-21-2-10-18kaz29-1250Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEУЛУЧШЕНИЕ ОПЕРАЦИОННОЙ ЭФФЕКТИВНОСТИ В ИНДУСТРИИ 4.0: ПОДХОД ПРЕДИКТИВНОГО ОБСЛУЖИВАНИЯENHANCING OPERATIONAL EFFICIENCY IN INDUSTRY 4.0: A PREDICTIVE MAINTENANCE APPROACHhttps://orcid.org/0009-0009-9182-2299АмангельдыИ. С.AmangeldyI. S.

магистрант

050000, г. Алматы

Master’s student

050000, Almaty

il_amangeldy@kbtu.kzhttps://orcid.org/0009-0001-3283-9826БисембаевА. С.BissembayevA. S.

профессор

050000, г. Алматы

Professor

050000, Almaty

a.bisembaev@kbtu.kzКазахстанско-Британский технический университетКазахстанKazakh-British Technical UniversityKazakhstan2024300620242121018Copyright © Амангельды И.С., Бисембаев А.С., 20242024Амангельды И.С., Бисембаев А.С.Amangeldy I.S., Bissembayev A.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/1250

Достижения индустрии 4.0 произвели революцию в производственных операциях, среди которых присутствует метод предиктивного обслуживания. Данный метод выступает одним из наиболее требовательных подходов за счет эффективной оптимизации графиков технического обслуживания и обеспечения продуктивной и бесперебойной работы. В статье представлен всесторонний обзор литературы, дающий представление о теоретических основах, исторических событиях и практическом применении прогнозного обслуживания. В разделе методологии подробно объясняется подход к исследованию, уделяется особое внимание разработке кода на основе MATLAB для создания прогнозной модели в соответствии с оставшимся сроком службы оборудования. Исследование применения предиктивного обслуживания проводится путем создания модели Байесовского вывода, основанной на корреляционном анализе Пирсона. Это исследование подчеркивает возможности прогнозной аналитики в повышении операционной точности и эффективности в различных отраслях. Поскольку спрос на надежные производственные процессы продолжает расти, результаты этого исследования дают представление о разработке передовых стратегий предиктивного обслуживания и достижении операционного совершенства с точки зрения интеллектуального производства.

Advancements of Industry 4.0 has revolutionized manufacturing operations, among them predictive maintenance (PdM) acts as one of the most demanding approaches. It effectively optimizes maintenance schedules and ensures efficient and uninterrupted work. Article provides a comprehensive literature review, offering insights into theoretical foundations, historical developments, and practical applications of predictive maintenance. The methodology section explains the research approach in detail, focusing on the development of a MATLAB-based code to generate the predictive model in accordance with the remaining useful life of the machine. Exploration into the application of PdM is made through the establishment of Bayesian Inference model informed by Pearson correlation analysis. This study underscores the possibilities of predictive analytics in enhancing operational accuracy and effectivity across various industries. As the demand for reliable manufacturing processes continues to grow, the findings of this research offer insights into the development of advanced PdM strategies and achievement of operational excellence in terms of smart manufacturing.

предиктивное обслуживаниесрок полезного использованияиндустрия 4.0надежностьpredictive maintenance (PdM)remaining useful life (RUL)industry 4.0reliabilityReferencesAlenizi F.A., Abbasi S., Mohammed A.H. and Rahmani A.M. (2023). Computers & Industrial Engineering, vol. 185, p. 109662. https://doi.org/10.1016/j.cie.2023.109662.Alenizi F.A., Abbasi S., Mohammed A.H. and Rahmani A.M. (2023). Computers & Industrial Engineering, vol. 185, p. 109662. https://doi.org/10.1016/j.cie.2023.109662.Okeme P.A., Skakun A.D. and Muzalevskii A.R. (2021) Transformation of factory to smart factory, Institute of Electrical and Electronics Engineering In., pp. 1499–1503.Okeme P.A., Skakun A.D. and Muzalevskii A.R. (2021) Transformation of factory to smart factory, Institute of Electrical and Electronics Engineering In., pp. 1499–1503.Zhang W., Yang D. and Wang H. (2019) IEEE Systems Journal, no. 13, pp. 2213–2227. https://doi.org/10.1109/JSYST.2019.2905565.Zhang W., Yang D. and Wang H. (2019) IEEE Systems Journal, no. 13, pp. 2213–2227. https://doi.org/10.1109/JSYST.2019.2905565.Raza A. and Ulansky V. (2017) Procedia CIRP, vol. 59, pp. 95–101. https://doi.org/10.1016/j.procir.2016.09.032.Raza A. and Ulansky V. (2017) Procedia CIRP, vol. 59, pp. 95–101. https://doi.org/10.1016/j.procir.2016.09.032.Elkateb S., Métwalli A., Shendy A. and Abu-Elanien A. (2024) Alexandria Engineering Journal, vol. 88, pp. 298–309. https://doi.org/10.1016/j.aej.2023.12.065.Elkateb S., Métwalli A., Shendy A. and Abu-Elanien A. (2024) Alexandria Engineering Journal, vol. 88, pp. 298–309. https://doi.org/10.1016/j.aej.2023.12.065.Fahrutdinov A. (2022). Ekibastuz was left without heat due to an accident at a thermal power plant. Kursiv Media Kazakhstan.Fahrutdinov A. (2022). Ekibastuz was left without heat due to an accident at a thermal power plant. Kursiv Media Kazakhstan.Mallioris P., Aivazidou E. and Bechtsis D. (2024) CIRP Journal of Manufacturing Science and Technology, vol. 50, pp. 80–103. https://doi.org/10.1016/j.cirpj.2024.02.003.Mallioris P., Aivazidou E. and Bechtsis D. (2024) CIRP Journal of Manufacturing Science and Technology, vol. 50, pp. 80–103. https://doi.org/10.1016/j.cirpj.2024.02.003.Gerum P.C., Altay A. and Baykal-Gürsoy M. (2019) Transportation Research Part C: Emerging Technologies, vol. 107, pp. 137–154. https://doi.org/10.1016/j.trc.2019.07.020.Gerum P.C., Altay A. and Baykal-Gürsoy M. (2019) Transportation Research Part C: Emerging Technologies, vol. 107, pp. 137–154. https://doi.org/10.1016/j.trc.2019.07.020.Hurtado J., Salvati D., Semola R., Bosio M. and Lomonaco V. (2023) Intelligent Systems with Applications, vol. 19, p. 200251. https://doi.org/10.1016/j.iswa.2023.200251.Hurtado J., Salvati D., Semola R., Bosio M. and Lomonaco V. (2023) Intelligent Systems with Applications, vol. 19, p. 200251. https://doi.org/10.1016/j.iswa.2023.200251.Zonta T., da Costa C.A., Righi R.R., de Lima M.J., da Trindade E.S. and Li G.P. (2020) Computers & Industrial Engineering, vol. 150, p. 106889. https://doi.org/10.1016/j.cie.2020.106889. 11 Cossu A., Graffieti G., Pellegrini L., Maltoni D., Bacciu D., Carta A. and Lomonaco V. (2022) Frontiers in Artificial Intelligence, vol. 5. https://doi.org/10.3389/frai.2022.829842.Zonta T., da Costa C.A., Righi R.R., de Lima M.J., da Trindade E.S. and Li G.P. (2020) Computers & Industrial Engineering, vol. 150, p. 106889. https://doi.org/10.1016/j.cie.2020.106889. 11 Cossu A., Graffieti G., Pellegrini L., Maltoni D., Bacciu D., Carta A. and Lomonaco V. (2022) Frontiers in Artificial Intelligence, vol. 5. https://doi.org/10.3389/frai.2022.829842.Lee S. M., Lee D. and Kim Y.S. (2019) The quality management ecosystem for predictive maintenance in the industry 4.0 era. International Journal of Quality Innovation, vol. 5, p. 4.Lee S. M., Lee D. and Kim Y.S. (2019) The quality management ecosystem for predictive maintenance in the industry 4.0 era. International Journal of Quality Innovation, vol. 5, p. 4.Liu J., Hou B., Lu M. and Wang D. (2024) Box-cox transformation based state-space modeling as a unified prognostic framework for degradation linearization and RUL prediction enhancement. Reliability Engineering Safety System, vol. 244, p. 109952.Liu J., Hou B., Lu M. and Wang D. (2024) Box-cox transformation based state-space modeling as a unified prognostic framework for degradation linearization and RUL prediction enhancement. Reliability Engineering Safety System, vol. 244, p. 109952.Sakib N. and Wuest T. (2018) Challenges and opportunities of condition-based predictive maintenance: a review. Procedia CIRP, vol. 78, pp. 267–272.Sakib N. and Wuest T. (2018) Challenges and opportunities of condition-based predictive maintenance: a review. Procedia CIRP, vol. 78, pp. 267–272.Peng Y., Dong M. and Zuo M.J. (2010) The International Journal of Advanced Manufacturing Technology, vol. 50, pp. 297–313. https://doi.org/10.1007/s00170-009-2482-0.Peng Y., Dong M. and Zuo M.J. (2010) The International Journal of Advanced Manufacturing Technology, vol. 50, pp. 297–313. https://doi.org/10.1007/s00170-009-2482-0.Spodniak M., Hovanec M. and Korba P. (2024) A novel method for the natural frequency estimation of the jet engine turbine blades based on its dimensions. Heliyon, vol. 10.Spodniak M., Hovanec M. and Korba P. (2024) A novel method for the natural frequency estimation of the jet engine turbine blades based on its dimensions. Heliyon, vol. 10.Lee W.J., Wu H., Yun H., Kim H., Jun M. and Sutherland J. (2019) Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP, vol. 80, pp. 506–511.Lee W.J., Wu H., Yun H., Kim H., Jun M. and Sutherland J. (2019) Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP, vol. 80, pp. 506–511.Raj P. (2021) Industrial use cases at the cusp of the IoT and blockchain paradigms, pp. 355–385.Raj P. (2021) Industrial use cases at the cusp of the IoT and blockchain paradigms, pp. 355–385.Huang L., Chen Y., Chen S. and Jiang H. (2012) Application of rcm analysis based predictive maintenance in nuclear power plants. 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, Chengdu, China, 15-18 June 2012. https://doi.org/10.1109/ICQR2MSE.2012.6246396.Huang L., Chen Y., Chen S. and Jiang H. (2012) Application of rcm analysis based predictive maintenance in nuclear power plants. 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, Chengdu, China, 15-18 June 2012. https://doi.org/10.1109/ICQR2MSE.2012.6246396.Feng Y., Sun L., Mo Z., Du M., Zhu C., Yang W. and Xu X. (2024) An evaluation of predictive correlations for the terminal rising velocity of a single bubble in quiescent clean liquid. International Journal of Multiphase Flow, vol. 173, p. 104736.Feng Y., Sun L., Mo Z., Du M., Zhu C., Yang W. and Xu X. (2024) An evaluation of predictive correlations for the terminal rising velocity of a single bubble in quiescent clean liquid. International Journal of Multiphase Flow, vol. 173, p. 104736.Shultz J. (2018) Measuring predictive maintenance program success. Reliable Plant. 22 Nguyen T.N. and Vilim R. B. (2023) Direct bayesian inference for fault severity assessment in digital-twin-based fault diagnosis. Annals of Nuclear Energy, vol. 194, p. 109932.Shultz J. (2018) Measuring predictive maintenance program success. Reliable Plant. 22 Nguyen T.N. and Vilim R. B. (2023) Direct bayesian inference for fault severity assessment in digital-twin-based fault diagnosis. Annals of Nuclear Energy, vol. 194, p. 109932.Pawan and Dhiman R. (2023) Electroencephologram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface. Measurement: Sensors, vol. 25, p. 100616.Pawan and Dhiman R. (2023) Electroencephologram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface. Measurement: Sensors, vol. 25, p. 100616.Fernandes M., Canito A., Bolon-Canedo V., Conceicao L., Praca I. and Marreiros G. (2019) Data analysis and feature selection for predictive maintenance: a case-study in the metallurgical industry. International Journal of Information Management, vol. 46, pp. 252–262.Fernandes M., Canito A., Bolon-Canedo V., Conceicao L., Praca I. and Marreiros G. (2019) Data analysis and feature selection for predictive maintenance: a case-study in the metallurgical industry. International Journal of Information Management, vol. 46, pp. 252–262.

The authors declare that there are no conflicts of interest present.