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Üretim Sistemlerinde Makine Öğrenmesi ile Kestirimci Bakım Uygulaması ve Modellemesi

Year 2022, Issue: 33, 167 - 175, 31.01.2022
https://doi.org/10.31590/ejosat.1019210

Abstract

Üretim sistemlerinin verimlilikleri söz konusu olduğunda bakım yaklaşımları son derece önemli bir role sahiptir. Geleneksel bakım yaklaşımları kısıtlı bir başarı sağlamış olsa da günümüz endüstriyel teknolojilerinin özellikle de Endüstri 4.0‘ın itici gücü ile birlikte makinelerden belirli standartlar ile veriler gerçek zamanlı okunabilmekte ve bu verilerle makine öğrenmesi (Machine Learning - ML) aracılığı ile bakım zamanları optimize edilebilmektedir. Böylece kestirimci bakım anlayışı ile üretim sürecindeki herhangi bir bakım gerektiren arıza önceden öngörülerek bu durum yaşanmadan önüne geçilebilmektedir. Bu çalışmada döküm fabrikasındaki makinede, makine öğrenmesi ile kestirimci bakım uygulamasını gerçekleştirebilmek amacıyla veriler 6 ay boyunca toplanmıştır. Elde edilen veriler ile temel bileşen analizi (Principal Component Analysis - PCA) ve rastgele orman (Random Forest - RF) makine öğrenmesi yöntemleri kullanılarak, sırasıyla denetimsiz ve denetimli olarak bakım zaman tahminleri %85,17 başarı oranı ile gerçekleştirilmiştir.

References

  • Agrawal, G. (2017). Should India Stay Away from the Fourth Revolution?. Available at SSRN 3084256. https://doi.org/10.2139/ssrn.3084256
  • Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2020). Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors, 20(1), 109. https://doi.org/10.3390/s20010109
  • Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598
  • Bektaş, O. Ğ. U. Z. (2020). Kestirimci Bakım İçin Döner Mekanizma Bozulma Eğrisinin Tanımlanması. Avrupa Bilim ve Teknoloji Dergisi, (19), 420-428. https://doi.org/10.31590/ejosat.708257
  • Calayır, G. N., & Kabak, M. (2021). Bakım için makine öğrenme tekniklerinin analizi ve bir uygulama. Journal of Turkish Operations Management, 5(1), 662-675.
  • Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
  • Cavalieri, S., & Cutuli, G. (2010, September). Performance evaluation of OPC UA. In 2010 IEEE 15th conference on emerging technologies & factory automation (ETFA 2010) (pp. 1-8). IEEE. https://doi.org/10.1109/ETFA.2010.5641184
  • Chazhoor, A., Mounika, Y., Sarobin, M. V. R., Sanjana, M. V., & Yasashvini, R. (2020, October). Predictive Maintenance using Machine Learning Based Classification Models. In IOP Conference Series: Materials Science and Engineering (Vol. 954, No. 1, p. 012001). IOP Publishing. https://doi.org/10.1088/1757-899X/954/1/012001
  • Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
  • Gedikli, T., ERVURAL, B. Ç., & ŞEN, D. T. (2021). Bulanık TOPSIS ve Bulanık AHP Yaklaşımlarıyla En Uygun Bakım Stratejisinin Belirlenmesi: Bir Gıda İşletmesinde Uygulama. Avrupa Bilim ve Teknoloji Dergisi, (22), 212-225. https://doi.org/10.31590/ejosat.838168
  • Kimera, D., & Nangolo, F. N. (2020). Predictive maintenance for ballast pumps on ship repair yards via machine learning. Transportation Engineering, 2, 100020. https://doi.org/10.1016/j.treng.2020.100020
  • Lauro, C. N., & Palumbo, F. (2000). Principal component analysis of interval data: a symbolic data analysis approach. Computational statistics, 15(1), 73-87. https://doi.org/10.1007/s001800050038
  • Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybala, J. (2016). A model-based method for remaining useful life prediction of machinery. IEEE Transactions on reliability, 65(3), 1314-1326. https://doi.org/10.1109/TR.2016.2570568.
  • Martins, J. P. S., Rodrigues, F. M., & Henriques, N. (2020). Modeling system based on machine learning approaches for predictive maintenance applications. KnE Engineering, 2020, 857-871. https://doi.org/10.18502/keg.v5i6.7105
  • Masani, K. I., Oza, P., & Agrawal, S. (2019). Predictive maintenance and monitoring of industrial machine using machine learning. Scalable Computing: Practice and Experience, 20(4), 663-668. https://doi.org/10.12694/scpe.v20i4.1585
  • Oktar, Ş. (2014). Demiryollarında araç bakım ve onarımı. Demiryolu Mühendisliği, (1), 38-40.
  • Özgür-Ünlüakın, D., Türkali, B., Karacaörenli, A., & Aksezer, S. Ç. (2019). A DBN based reactive maintenance model for a complex system in thermal power plants. Reliability Engineering & System Safety, 190, 106505. https://doi.org/10.1016/j.ress.2019.106505
  • Özkat, E. C. (2021). Makine Öğrenmesi Metodolojisi Kullanılarak Yüksek Hızlı Rulmanlarda Sağlık Göstergesinin Belirlenmesi. Avrupa Bilim ve Teknoloji Dergisi, (22), 176-183. https://doi.org/10.31590/ejosat.869285
  • Sirvio, K. M. (2015). Intelligent Systems in Maintenance Planning and Management. In Intelligent Techniques in Engineering Management (pp. 221-245). Springer, Cham. https://doi.org/10.1007/978-3-319-17906-3_10
  • Tahan, M., Tsoutsanis, E., Muhammad, M., & Karim, Z. A. (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied energy, 198, 122-144. https://doi.org/10.1016/j.apenergy.2017.04.048
  • Turker, B. B., Yemez, Y., Sezgin, T. M., & Erzin, E. (2017). Audio-facial laughter detection in naturalistic dyadic conversations. IEEE Transactions on Affective Computing, 8(4), 534-545. https://doi.org/10.1109/TAFFC.2017.2754256.
  • Wang, Q., Bu, S., & He, Z. (2020). Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Transactions on Industrial Informatics, 16(10), 6509-6517. https://doi.org/10.1109/TII.2020.2966033
  • Yeşilkanat, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, 110210. https://doi.org/10.1016/j.chaos.2020.110210
  • Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 106889. https://doi.org/10.1016/j.cie.2020.106889

Predictive Maintenance Application and Modeling With Machine Learning In Production Systems

Year 2022, Issue: 33, 167 - 175, 31.01.2022
https://doi.org/10.31590/ejosat.1019210

Abstract

Maintenance approaches have an extremely important role when it comes to efficiency in production systems. Although traditional maintenance approaches have had limited success, with the driving force of today's industrial technologies, especially Industry 4.0, all maintenance data generated on production machines can be collected in real time, and maintenance times can be optimized through machine learning. Thus, with the predictive maintenance approach, any maintenance-required breakdown in the production process can be foreseen and prevented before this situation occurs. In this study, data were collected for 6 months in order to implement predictive maintenance with machine learning on the production machine in the casting factory. With the obtained data, using PCA (Principal Component Analysis) and Random Forest machine learning methods, unsupervised and supervised maintenance time estimations were performed with an accuracy of 85.17%.

References

  • Agrawal, G. (2017). Should India Stay Away from the Fourth Revolution?. Available at SSRN 3084256. https://doi.org/10.2139/ssrn.3084256
  • Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2020). Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors, 20(1), 109. https://doi.org/10.3390/s20010109
  • Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598
  • Bektaş, O. Ğ. U. Z. (2020). Kestirimci Bakım İçin Döner Mekanizma Bozulma Eğrisinin Tanımlanması. Avrupa Bilim ve Teknoloji Dergisi, (19), 420-428. https://doi.org/10.31590/ejosat.708257
  • Calayır, G. N., & Kabak, M. (2021). Bakım için makine öğrenme tekniklerinin analizi ve bir uygulama. Journal of Turkish Operations Management, 5(1), 662-675.
  • Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
  • Cavalieri, S., & Cutuli, G. (2010, September). Performance evaluation of OPC UA. In 2010 IEEE 15th conference on emerging technologies & factory automation (ETFA 2010) (pp. 1-8). IEEE. https://doi.org/10.1109/ETFA.2010.5641184
  • Chazhoor, A., Mounika, Y., Sarobin, M. V. R., Sanjana, M. V., & Yasashvini, R. (2020, October). Predictive Maintenance using Machine Learning Based Classification Models. In IOP Conference Series: Materials Science and Engineering (Vol. 954, No. 1, p. 012001). IOP Publishing. https://doi.org/10.1088/1757-899X/954/1/012001
  • Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
  • Gedikli, T., ERVURAL, B. Ç., & ŞEN, D. T. (2021). Bulanık TOPSIS ve Bulanık AHP Yaklaşımlarıyla En Uygun Bakım Stratejisinin Belirlenmesi: Bir Gıda İşletmesinde Uygulama. Avrupa Bilim ve Teknoloji Dergisi, (22), 212-225. https://doi.org/10.31590/ejosat.838168
  • Kimera, D., & Nangolo, F. N. (2020). Predictive maintenance for ballast pumps on ship repair yards via machine learning. Transportation Engineering, 2, 100020. https://doi.org/10.1016/j.treng.2020.100020
  • Lauro, C. N., & Palumbo, F. (2000). Principal component analysis of interval data: a symbolic data analysis approach. Computational statistics, 15(1), 73-87. https://doi.org/10.1007/s001800050038
  • Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybala, J. (2016). A model-based method for remaining useful life prediction of machinery. IEEE Transactions on reliability, 65(3), 1314-1326. https://doi.org/10.1109/TR.2016.2570568.
  • Martins, J. P. S., Rodrigues, F. M., & Henriques, N. (2020). Modeling system based on machine learning approaches for predictive maintenance applications. KnE Engineering, 2020, 857-871. https://doi.org/10.18502/keg.v5i6.7105
  • Masani, K. I., Oza, P., & Agrawal, S. (2019). Predictive maintenance and monitoring of industrial machine using machine learning. Scalable Computing: Practice and Experience, 20(4), 663-668. https://doi.org/10.12694/scpe.v20i4.1585
  • Oktar, Ş. (2014). Demiryollarında araç bakım ve onarımı. Demiryolu Mühendisliği, (1), 38-40.
  • Özgür-Ünlüakın, D., Türkali, B., Karacaörenli, A., & Aksezer, S. Ç. (2019). A DBN based reactive maintenance model for a complex system in thermal power plants. Reliability Engineering & System Safety, 190, 106505. https://doi.org/10.1016/j.ress.2019.106505
  • Özkat, E. C. (2021). Makine Öğrenmesi Metodolojisi Kullanılarak Yüksek Hızlı Rulmanlarda Sağlık Göstergesinin Belirlenmesi. Avrupa Bilim ve Teknoloji Dergisi, (22), 176-183. https://doi.org/10.31590/ejosat.869285
  • Sirvio, K. M. (2015). Intelligent Systems in Maintenance Planning and Management. In Intelligent Techniques in Engineering Management (pp. 221-245). Springer, Cham. https://doi.org/10.1007/978-3-319-17906-3_10
  • Tahan, M., Tsoutsanis, E., Muhammad, M., & Karim, Z. A. (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied energy, 198, 122-144. https://doi.org/10.1016/j.apenergy.2017.04.048
  • Turker, B. B., Yemez, Y., Sezgin, T. M., & Erzin, E. (2017). Audio-facial laughter detection in naturalistic dyadic conversations. IEEE Transactions on Affective Computing, 8(4), 534-545. https://doi.org/10.1109/TAFFC.2017.2754256.
  • Wang, Q., Bu, S., & He, Z. (2020). Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Transactions on Industrial Informatics, 16(10), 6509-6517. https://doi.org/10.1109/TII.2020.2966033
  • Yeşilkanat, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, 110210. https://doi.org/10.1016/j.chaos.2020.110210
  • Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 106889. https://doi.org/10.1016/j.cie.2020.106889
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Hakan Ceyhan 0000-0003-1776-4827

Mustafa Cem Kasapbaşı 0000-0001-6444-6659

Early Pub Date January 30, 2022
Publication Date January 31, 2022
Published in Issue Year 2022 Issue: 33

Cite

APA Ceyhan, H., & Kasapbaşı, M. C. (2022). Üretim Sistemlerinde Makine Öğrenmesi ile Kestirimci Bakım Uygulaması ve Modellemesi. Avrupa Bilim Ve Teknoloji Dergisi(33), 167-175. https://doi.org/10.31590/ejosat.1019210