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KESTİRİMCİ BAKIM PLANLAMA İÇİN MAKİNE ÖĞRENMESİ TEMELLİ BİR KARAR DESTEK SİSTEMİ VE BİR UYGULAMA

Year 2022, DİJİTAL DÖNÜŞÜM VE VERİMLİLİK, 48 - 66, 12.01.2022
https://doi.org/10.51551/verimlilik.988104

Abstract

Amaç: Üretim sistemlerinde meydana gelen arızaları önlemek için Endüstri 4.0 altyapısını kullanan kestirimci bakım planlama işletmelerin gündemine girmiştir. Bu çalışmada, bir sistemde meydana gelen arızaların ve üretim duruşlarının en küçüklenmesi için nesnelerin interneti (IoT) ve makine öğrenmesi tabanlı bakım karar destek sistemi oluşturulmuş ve bir makine üzerinde pilot çalışma yapılmıştır.


Yöntem:
Bu çalışmada, sistemin sürekli izlenebilirliğini sağlamak için sıcaklık, nem ve ses sensörleri kullanılmıştır. Bu sensörlerle alınan veriler IoT kullanılarak veri tabanına bir ağ aracılığı ile aktarılmıştır. Aktarılan bu verilerden sistemin durumunu (“arıza olabilir”, “sağlam”) tahmin etmek için makine öğrenmesi teknikleri (Destek Vektör Makinesi ve Karar Ağacı) kullanılmıştır. Geçmiş arıza kayıtları ve geçmiş üretim planları birleştirilerek makineye gelen ürün sırasının arızaya etkisi sıralı örüntü madenciliği yöntemleri ile araştırılmıştır.


Bulgular:
Geliştirilen karar destek sistemi, bakım kararı verebilmektedir. Böylece pilot çalışma yapılan makinede gerçekleşmiş olan 1419 dk. beklenmeyen duruşların en küçüklenmesi sağlanacaktır.


Özgünlük:
Yenilikçi bir yön olarak; sisteme giren ürün sırasının da arızaya etkisinin olabileceği sıralı örüntü madenciliği yöntemleriyle incelenmiştir. IoT, makine öğrenmesi, kestirimci bakım, sıralı örüntü madenciliği ve dinamik çizelgelemenin entegrasyonunu içeren bir bakım karar destek sistemi oluşturulmuştur.

Supporting Institution

Tübitak

Project Number

2209-B

Thanks

Bu çalışma Tübitak 2209-B Sanayiye Yönelik Lisans Araştırma Projeleri Destekleme Programı tarafından 2021 yılında desteklenmiştir.

References

  • Aktürk, M.S. ve Görgülü, E. (1999). “Match-up Scheduling under a Machine Breakdown”, European Journal of Operational Research, 112(1), 81-97.
  • Arena, S., Florian, E., Zennaro, I., Orrù, P.F., ve Sgarbossa, F. (2022). “A Novel Decision Support System for Managing Predictive Maintenance Strategies Based on Machine Learning Approaches”, Safety Science, 146, 105529.
  • Baykasoğlu, A., Madenoğlu, F.S., ve Hamzadayı, A. (2020). “Greedy Randomized Adaptive Search for Dynamic Flexible Job-Shop Scheduling”, Journal of Manufacturing Systems, 56, 425-451.
  • Boser, B.E., Guyon, I.M., ve Vapnik, V.N. (1992). “A Training Algorithm for Optimal Margin Classifiers”. Proceedings of the 5th Annual Workshop on Computational Learning Theory, 144-152.
  • Breiman, L., Friedman, J.H., Olshen, R.A., ve Stone, C.J. (1984). “Classification and Regression Trees”. Wadsworth ve Brooks, Cole Statistics/Probability Series.
  • Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., ve Alcalá, S.G. (2019). “A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance”, Computers & Industrial Engineering, 137, 106024.
  • Cortes, C. ve Vapnik, V. (1995). “Support-Vector Networks”, Machine Learning, 20(3), 273-297.
  • Cowling, P. ve Johansson, M. (2002). “Using Real Time Information for Effective Dynamic Scheduling”, European Journal of Operational Research, 139(2), 230-244.
  • Çakır, M., Güvenç, M.A. ve Mıstıkoğlu, S. (2021). “The Experimental Application of Popular Machine Learning Algorithms on Predictive Maintenance and the Design of IoT Based Condition Monitoring System”, Computers & Industrial Engineering, 151, 106948.
  • Çınar, Z.M., Nuhu, A.A., Zeeshan, Q., Korhan, O., Asmael, M. ve Safaei, B. (2020). “Machine Learning in Predictive Maintenance Towards Sustainable Smart Manufacturing in Industry 4.0”. Sustainability, 12(19), 8211.
  • Çolak, M., Çetin, T. ve Atılgan, A. (2017). “Mobilya Endüstrisinde Tamir Bakımın Önemi ve Bir Uygulama”, Akademia Mühendislik ve Fen Bilimleri Dergisi, 2(3), 60-70.
  • Dangut, M.D., Skaf, Z. ve Jennions, I.K. (2021). “An Integrated Machine Learning Model for Aircraft Components Rare Failure Prognostics with Log-Based Dataset”, ISA Transactions, 113, 127-139.
  • Doğan, A., ve Birant, D. (2021). “Machine Learning and Data Mining in Manufacturing”, Expert Systems with Applications, 166, 114060.
  • Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., ve Barbosa, J. (2020). “Machine Learning and Reasoning for Predictive Maintenance in Industry 4.0: Current Status and Challenges”, Computers in Industry, 123, 103298.
  • Dos Santos, T., Ferreira, F.J., Pires, J.M., ve Damásio, C. (2017). “Stator Winding Short-Circuit Fault Diagnosis in Induction Motors Using Random Forest”, 2017 IEEE International Electric Machines and Drives Conference (IEMDC), 1-8. Eroğlu, A. (1998). “Planlı Bakım Sistemleri İçin Bazı Stokastik Yenileme Modelleri”, Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 13(2), 173-184.
  • Fang, J. ve Xi, Y. (1997). “A Rolling Horizon Job Shop Rescheduling Strategy in the Dynamic Environment”, The International Journal of Advanced Manufacturing Technology, 13(3), 227-232.
  • Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S. ve Thomas, R. (2017). “A Survey of Sequential Pattern Mining”, Data Science and Pattern Recognition, 1(1), 54-77.
  • Kang, Z., Catal, C. ve Tekinerdogan, B. (2020). “Machine Learning Applications in Production Lines: A Systematic Literature Review”, Computers & Industrial Engineering, 149, 106773.
  • Karaduman, G. (2020). “Raylı Sistemlerde Bilgisayarlı Görme ve Nesnelerin İnterneti Kullanılarak Kestirimci Bakım Yöntemlerinin Geliştirilmesi”, Doktora Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü, Elazığ.
  • Koçer, M. (2017). “CNC Kesim Makinesi İçin Mükemmel Olmayan Önleyici Bakım Politikasının Geliştirilmesi ve En İyilenmesi”, Yüksek Lisans Tezi, TOBB ETÜ Fen Bilimleri Enstitüsü, Ankara.
  • Köksal, M. ve Uzun, A. (2016). “Bakım Planlaması”, Seçkin Yayıncılık, Ankara.
  • Kulkarni, K., Devi, U., Sirighee, A., Hazra, J. ve Rao, P. (2018). “Predictive Maintenance for Supermarket Refrigeration Systems Using Only Case Temperature Data”, 2018 Annual American Control Conference (ACC), 4640-4645.
  • Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N. ve Nandi, A.K. (2020). “Applications of Machine Learning to Machine Fault Diagnosis: A Review and Roadmap”, Mechanical Systems and Signal Processing, 138, 106587.
  • Li, Z., ve He, Q. (2015). “Prediction of Railcar Remaining Useful Life by Multiple Data Source Fusion”, IEEE Transactions on Intelligent Transportation Systems, 16(4), 2226-2235.
  • Lim, H.W., Kim, Y. ve Kim, M.K. (2017). “Failure Prediction Using Sequential Pattern Mining in the Wire Bonding Process”, IEEE Transactions on Semiconductor Manufacturing, 30(3), 285-292.
  • Liu, Q., Dong, M., Chen, F.F., Lv, W. ve Ye, C. (2019). “Single-Machine-Based Joint Optimization of Predictive Maintenance Planning and Production Scheduling”, Robotics and Computer-Integrated Manufacturing, 55, 173-182.
  • Lu, Y. (2017). “Industry 4.0: A Survey on Technologies, Applications and Open Research Issues”, Journal of Industrial Information Integration, 6, 1-10.
  • Mehta, S.V. ve Uzsoy, R. (1999). “Predictable Scheduling of a Single Machine Subject to Breakdowns”, International Journal of Computer Integrated Manufacturing, 12(1), 15-38.
  • O'donovan, R., Uzsoy, R. ve McKay, K.N. (1999). “Predictable Scheduling of a Single Machine with Breakdowns and Sensitive Jobs”, International Journal of Production Research, 37(18), 4217-4233.
  • Ouelhadj, D. ve Petrovic, S. (2009). “A Survey of Dynamic Scheduling in Manufacturing Systems”, Journal of Scheduling, 12(4), 417-431.
  • Pamuk, N.S. ve Soysal, M. (2018). “Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme”. Verimlilik Dergisi, 1, 41-66.
  • Pan, E., Liao, W. ve Xi, L. (2012). “A Joint Model of Production Scheduling and Predictive Maintenance for Minimizing Job Tardiness”, The International Journal of Advanced Manufacturing Technology, 60(9-12), 1049-1061. Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U. ve Hsu, M.C. (2004). “Mining Sequential Patterns by Pattern-Growth: The Prefixspan Approach”, IEEE Transactions on Knowledge and Data Engineering, 16(11), 1424-1440.
  • Quinlan, J.R. (1993). “C4.5: Programs for Machine Learning”, Morgan-Kaufmann, San Francisco. Rezig, S., Achour, Z. ve Rezg, N. (2019). “Using Data Mining Methods for Predicting Sequential Maintenance Activities”. Applied Sciences, 8(11), 2184.
  • Sabuncuoğlu, I. ve Bayız, M. (2000). “Analysis of Reactive Scheduling Problems in a Job Shop Environment”, European Journal of Operational Research, 126(3), 567-586.
  • Sezer, E., Romero, D., Guedea, F., Macchi, M. ve Emmanouilidis, C. (2018). “An industry 4.0-Enabled Low Cost Predictive Maintenance Approach for SMEs”, 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 1-8.
  • Srikant, R. ve Agrawal, R. (1996). “Mining Sequential Patterns: Generalizations and Performance Improvements”, International Conference on Extending Database Technology, Springer, Berlin, Heidelberg, 1-17.
  • Susto, G.A., Schirru, A., Pampuri, S., McLoone, S. ve Beghi, A. (2015). “Machine Learning for Predictive Maintenance: A Multiple Classifier Approach”, IEEE Transactions on Industrial Informatics, 11, 812-820.
  • Susto, G.A. Beghi, A. ve De Luca, C. A. (2012). “Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques”, Transactions on Semiconductor Manufacturing, 25, 638-649.
  • Uhlmann, E., Pontes, R. P., Geisert, C. ve Hohwieler, E. (2018). “Cluster Identification of Sensor Data for Predictive Maintenance in a Selective Laser Melting Machine Tool”, Procedia Manufacturing, 24, 60-65.
  • Vieira, G.E., Herrmann, J.W. ve Lin, E. (2003). “Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods”, Journal of Scheduling, 6(1), 39-62.
  • Wuest, T., Weimer, D., Irgens, C. ve Thoben, K.D. (2016). “Machine Learning in Manufacturing: Advantages, Challenges, and Applications”, Production ve Manufacturing Research, 4(1), 23-45.
  • Yan, X., Han, J. ve Afshar, R. (2003). “Clospan: Mining: Closed Sequential Patterns in Large Datasets”, Proceedings of the 2003 SIAM International Conference on Data Mining, 166-177.
  • Zaki, M.J. (2001). “SPADE: An Efficient Algorithm for Mining Frequent Sequences”, Machine Learning, 42(1), 31-60.
  • Zhai, S., Gehring, B. ve Reinhart, G. (2021). “Enabling Predictive Maintenance Integrated Production Scheduling by Operation-Specific Health Prognostics with Generative Deep Learning”, Journal of Manufacturing Systems, 61, 830-855.
  • Zhang, J., Ding, G., Zou, Y., Qin, S. ve Fu, J. (2019). “Review of Job Shop Scheduling Research and Its New Perspectives Under Industry 4.0”, Journal of Intelligent Manufacturing, 30(4), 1809-1830.
  • Zonta, T., Da Costa, C.A., Da Rosa Righi, R., De Lima, M.J., Da Trindade, E.S. ve Li, G.P. (2020). “Predictive Maintenance in the Industry 4.0: A Systematic Literature Review”, Computers ve Industrial Engineering, 106889.

A MACHINE LEARNING-BASED DECISION SUPPORT SYSTEM FOR PREDICTIVE MAINTENANCE PLANNING AND AN APPLICATION

Year 2022, DİJİTAL DÖNÜŞÜM VE VERİMLİLİK, 48 - 66, 12.01.2022
https://doi.org/10.51551/verimlilik.988104

Abstract

Purpose: In order to prevent breakdowns in production systems, predictive maintenance planning using Industry 4.0 infrastructure has been the focus of companies. In this study, a predictive maintenance decision support system integrated with internet-of-things (IoT) was developed and a pilot study was carried out on a machine to minimize the breakdowns and production downtime.

Methodology: Temperature, humidity, and sound sensors have been used in order to provide continuous monitoring of the system. The data obtained with these sensors is transferred to a database via a network using IoT. In order to predict the system state (“breakdown may occur”, “good”) from this data, the machine learning techniques (Support Vector Machine and Decision Tree) are used. Historical breakdown records and production plan information are merged in order to find out the effect of production schedule on machine breakdowns.


Findings:
The proposed decision support system is able to make self-maintenance decision. Thus, it would be possible to minimize 1419 min. downtime of the machine that the pilot study was performed on.


Originality:
The effect of production sequence on system breakdowns has been investigated with sequential pattern mining algorithms. A maintenance decision support system including the integration of IoT, machine learning, predictive maintenance, sequential pattern mining and dynamic scheduling has been developed.

Project Number

2209-B

References

  • Aktürk, M.S. ve Görgülü, E. (1999). “Match-up Scheduling under a Machine Breakdown”, European Journal of Operational Research, 112(1), 81-97.
  • Arena, S., Florian, E., Zennaro, I., Orrù, P.F., ve Sgarbossa, F. (2022). “A Novel Decision Support System for Managing Predictive Maintenance Strategies Based on Machine Learning Approaches”, Safety Science, 146, 105529.
  • Baykasoğlu, A., Madenoğlu, F.S., ve Hamzadayı, A. (2020). “Greedy Randomized Adaptive Search for Dynamic Flexible Job-Shop Scheduling”, Journal of Manufacturing Systems, 56, 425-451.
  • Boser, B.E., Guyon, I.M., ve Vapnik, V.N. (1992). “A Training Algorithm for Optimal Margin Classifiers”. Proceedings of the 5th Annual Workshop on Computational Learning Theory, 144-152.
  • Breiman, L., Friedman, J.H., Olshen, R.A., ve Stone, C.J. (1984). “Classification and Regression Trees”. Wadsworth ve Brooks, Cole Statistics/Probability Series.
  • Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.D.P., Basto, J.P., ve Alcalá, S.G. (2019). “A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance”, Computers & Industrial Engineering, 137, 106024.
  • Cortes, C. ve Vapnik, V. (1995). “Support-Vector Networks”, Machine Learning, 20(3), 273-297.
  • Cowling, P. ve Johansson, M. (2002). “Using Real Time Information for Effective Dynamic Scheduling”, European Journal of Operational Research, 139(2), 230-244.
  • Çakır, M., Güvenç, M.A. ve Mıstıkoğlu, S. (2021). “The Experimental Application of Popular Machine Learning Algorithms on Predictive Maintenance and the Design of IoT Based Condition Monitoring System”, Computers & Industrial Engineering, 151, 106948.
  • Çınar, Z.M., Nuhu, A.A., Zeeshan, Q., Korhan, O., Asmael, M. ve Safaei, B. (2020). “Machine Learning in Predictive Maintenance Towards Sustainable Smart Manufacturing in Industry 4.0”. Sustainability, 12(19), 8211.
  • Çolak, M., Çetin, T. ve Atılgan, A. (2017). “Mobilya Endüstrisinde Tamir Bakımın Önemi ve Bir Uygulama”, Akademia Mühendislik ve Fen Bilimleri Dergisi, 2(3), 60-70.
  • Dangut, M.D., Skaf, Z. ve Jennions, I.K. (2021). “An Integrated Machine Learning Model for Aircraft Components Rare Failure Prognostics with Log-Based Dataset”, ISA Transactions, 113, 127-139.
  • Doğan, A., ve Birant, D. (2021). “Machine Learning and Data Mining in Manufacturing”, Expert Systems with Applications, 166, 114060.
  • Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., ve Barbosa, J. (2020). “Machine Learning and Reasoning for Predictive Maintenance in Industry 4.0: Current Status and Challenges”, Computers in Industry, 123, 103298.
  • Dos Santos, T., Ferreira, F.J., Pires, J.M., ve Damásio, C. (2017). “Stator Winding Short-Circuit Fault Diagnosis in Induction Motors Using Random Forest”, 2017 IEEE International Electric Machines and Drives Conference (IEMDC), 1-8. Eroğlu, A. (1998). “Planlı Bakım Sistemleri İçin Bazı Stokastik Yenileme Modelleri”, Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 13(2), 173-184.
  • Fang, J. ve Xi, Y. (1997). “A Rolling Horizon Job Shop Rescheduling Strategy in the Dynamic Environment”, The International Journal of Advanced Manufacturing Technology, 13(3), 227-232.
  • Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S. ve Thomas, R. (2017). “A Survey of Sequential Pattern Mining”, Data Science and Pattern Recognition, 1(1), 54-77.
  • Kang, Z., Catal, C. ve Tekinerdogan, B. (2020). “Machine Learning Applications in Production Lines: A Systematic Literature Review”, Computers & Industrial Engineering, 149, 106773.
  • Karaduman, G. (2020). “Raylı Sistemlerde Bilgisayarlı Görme ve Nesnelerin İnterneti Kullanılarak Kestirimci Bakım Yöntemlerinin Geliştirilmesi”, Doktora Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü, Elazığ.
  • Koçer, M. (2017). “CNC Kesim Makinesi İçin Mükemmel Olmayan Önleyici Bakım Politikasının Geliştirilmesi ve En İyilenmesi”, Yüksek Lisans Tezi, TOBB ETÜ Fen Bilimleri Enstitüsü, Ankara.
  • Köksal, M. ve Uzun, A. (2016). “Bakım Planlaması”, Seçkin Yayıncılık, Ankara.
  • Kulkarni, K., Devi, U., Sirighee, A., Hazra, J. ve Rao, P. (2018). “Predictive Maintenance for Supermarket Refrigeration Systems Using Only Case Temperature Data”, 2018 Annual American Control Conference (ACC), 4640-4645.
  • Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N. ve Nandi, A.K. (2020). “Applications of Machine Learning to Machine Fault Diagnosis: A Review and Roadmap”, Mechanical Systems and Signal Processing, 138, 106587.
  • Li, Z., ve He, Q. (2015). “Prediction of Railcar Remaining Useful Life by Multiple Data Source Fusion”, IEEE Transactions on Intelligent Transportation Systems, 16(4), 2226-2235.
  • Lim, H.W., Kim, Y. ve Kim, M.K. (2017). “Failure Prediction Using Sequential Pattern Mining in the Wire Bonding Process”, IEEE Transactions on Semiconductor Manufacturing, 30(3), 285-292.
  • Liu, Q., Dong, M., Chen, F.F., Lv, W. ve Ye, C. (2019). “Single-Machine-Based Joint Optimization of Predictive Maintenance Planning and Production Scheduling”, Robotics and Computer-Integrated Manufacturing, 55, 173-182.
  • Lu, Y. (2017). “Industry 4.0: A Survey on Technologies, Applications and Open Research Issues”, Journal of Industrial Information Integration, 6, 1-10.
  • Mehta, S.V. ve Uzsoy, R. (1999). “Predictable Scheduling of a Single Machine Subject to Breakdowns”, International Journal of Computer Integrated Manufacturing, 12(1), 15-38.
  • O'donovan, R., Uzsoy, R. ve McKay, K.N. (1999). “Predictable Scheduling of a Single Machine with Breakdowns and Sensitive Jobs”, International Journal of Production Research, 37(18), 4217-4233.
  • Ouelhadj, D. ve Petrovic, S. (2009). “A Survey of Dynamic Scheduling in Manufacturing Systems”, Journal of Scheduling, 12(4), 417-431.
  • Pamuk, N.S. ve Soysal, M. (2018). “Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme”. Verimlilik Dergisi, 1, 41-66.
  • Pan, E., Liao, W. ve Xi, L. (2012). “A Joint Model of Production Scheduling and Predictive Maintenance for Minimizing Job Tardiness”, The International Journal of Advanced Manufacturing Technology, 60(9-12), 1049-1061. Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U. ve Hsu, M.C. (2004). “Mining Sequential Patterns by Pattern-Growth: The Prefixspan Approach”, IEEE Transactions on Knowledge and Data Engineering, 16(11), 1424-1440.
  • Quinlan, J.R. (1993). “C4.5: Programs for Machine Learning”, Morgan-Kaufmann, San Francisco. Rezig, S., Achour, Z. ve Rezg, N. (2019). “Using Data Mining Methods for Predicting Sequential Maintenance Activities”. Applied Sciences, 8(11), 2184.
  • Sabuncuoğlu, I. ve Bayız, M. (2000). “Analysis of Reactive Scheduling Problems in a Job Shop Environment”, European Journal of Operational Research, 126(3), 567-586.
  • Sezer, E., Romero, D., Guedea, F., Macchi, M. ve Emmanouilidis, C. (2018). “An industry 4.0-Enabled Low Cost Predictive Maintenance Approach for SMEs”, 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 1-8.
  • Srikant, R. ve Agrawal, R. (1996). “Mining Sequential Patterns: Generalizations and Performance Improvements”, International Conference on Extending Database Technology, Springer, Berlin, Heidelberg, 1-17.
  • Susto, G.A., Schirru, A., Pampuri, S., McLoone, S. ve Beghi, A. (2015). “Machine Learning for Predictive Maintenance: A Multiple Classifier Approach”, IEEE Transactions on Industrial Informatics, 11, 812-820.
  • Susto, G.A. Beghi, A. ve De Luca, C. A. (2012). “Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques”, Transactions on Semiconductor Manufacturing, 25, 638-649.
  • Uhlmann, E., Pontes, R. P., Geisert, C. ve Hohwieler, E. (2018). “Cluster Identification of Sensor Data for Predictive Maintenance in a Selective Laser Melting Machine Tool”, Procedia Manufacturing, 24, 60-65.
  • Vieira, G.E., Herrmann, J.W. ve Lin, E. (2003). “Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods”, Journal of Scheduling, 6(1), 39-62.
  • Wuest, T., Weimer, D., Irgens, C. ve Thoben, K.D. (2016). “Machine Learning in Manufacturing: Advantages, Challenges, and Applications”, Production ve Manufacturing Research, 4(1), 23-45.
  • Yan, X., Han, J. ve Afshar, R. (2003). “Clospan: Mining: Closed Sequential Patterns in Large Datasets”, Proceedings of the 2003 SIAM International Conference on Data Mining, 166-177.
  • Zaki, M.J. (2001). “SPADE: An Efficient Algorithm for Mining Frequent Sequences”, Machine Learning, 42(1), 31-60.
  • Zhai, S., Gehring, B. ve Reinhart, G. (2021). “Enabling Predictive Maintenance Integrated Production Scheduling by Operation-Specific Health Prognostics with Generative Deep Learning”, Journal of Manufacturing Systems, 61, 830-855.
  • Zhang, J., Ding, G., Zou, Y., Qin, S. ve Fu, J. (2019). “Review of Job Shop Scheduling Research and Its New Perspectives Under Industry 4.0”, Journal of Intelligent Manufacturing, 30(4), 1809-1830.
  • Zonta, T., Da Costa, C.A., Da Rosa Righi, R., De Lima, M.J., Da Trindade, E.S. ve Li, G.P. (2020). “Predictive Maintenance in the Industry 4.0: A Systematic Literature Review”, Computers ve Industrial Engineering, 106889.
There are 46 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Banu Soylu 0000-0003-4164-7583

Hatice Yiğiter This is me 0000-0002-9269-0111

Venüs Sarıkaya This is me 0000-0002-0429-948X

Zinnet Sandıkçı This is me 0000-0003-0968-5010

Asena Utku This is me 0000-0001-6793-1369

Project Number 2209-B
Publication Date January 12, 2022
Submission Date August 28, 2021
Published in Issue Year 2022 DİJİTAL DÖNÜŞÜM VE VERİMLİLİK

Cite

APA Soylu, B., Yiğiter, H., Sarıkaya, V., Sandıkçı, Z., et al. (2022). KESTİRİMCİ BAKIM PLANLAMA İÇİN MAKİNE ÖĞRENMESİ TEMELLİ BİR KARAR DESTEK SİSTEMİ VE BİR UYGULAMA. Verimlilik Dergisi48-66. https://doi.org/10.51551/verimlilik.988104

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