Abstract
Bakım, her üretim kuruluşunda olması gerekli bir faaliyet olarak kabul edilirken, günümüzde ise ilave olarak şirketin gelir ve giderlerini etkileyen
kritik bir işletme fonksiyonu olarak tanımlanmaktadır. Makine öğrenmesi kavramı, makinelerin karşılaştıkları durumlar karşısında kendini eğiterek
daha iyi kararlar verebilmesini sağlayan algoritmaların geliştirilmesi olgusudur. 1950 yıllarından itibaren bakım planlaması tahminleme
çalışmasında makine öğrenmesi teknikleri kullanılmaktadır. Savunma sanayi firmasında yapılan bu çalışmada makinelerin aniden ve plansız
yapılan bakımlardan kaynaklı maliyeti yüksek olan parçaların hurda olması ve sevkiyatlarda meydana gelen gecikmelerden dolayı firmanın
müşterilere yüksek miktarda ceza ödemesi problemi ele alınmıştır. Bu çalışmadaki amaç gelişen bilim ve teknoloji kullanılarak, yapılacak olan
bakım planlamalarını, arızaları önceden tahmin etmek, üretimde durmayı, maliyet kaybını en aza indirgemek veya tamamen engelleyebilmektir.
Makine Öğrenmesi tekniklerinden denetimli öğrenme tekniği savunma sanayi firmasındaki en kritik kimyasal boya makinesinde uygulanarak
bakım planlaması tahmini çalışması yapılmıştır.
References
- Vapnik, V. (2000). The Nature of Statistical Learning Theory, Second Ed., 1, New York.
- Nils J. Nilsson, (1996). Introduction to Machine Learning: An Early Draft of a Proposed Textbook, Stanford University.
- Nourmohammadzadeh, A., Hartmann, S. (2015). Fault Classification of a Centrifugal Pump in Normal and Noisy Environment with Artificial Neural Network and Support Vector Machine Enhanced by a Genetic Algorithm in Theory and Practice of Natural Computing. Lecture Notes in Computer Science.
- Hashemian, H., Bean, W. C. (2011). “State-of-the-art predictive maintenance techniques,” IEEE Trans. Instrum. Meas., 60, 3480–3492
- Jahnke, P. (2015). Machine Learning Approaches for Failure Type Detection and Predictive Maintenance, 83.
- Garcia, M. A., Sanz-Bobi, Pico, J. (2006). SIMAP: Intelligent System for Predictive Maintenance. “Application to the health condition monitoring of a wind turbine gearbox”, Comput. Ind., 57, 552–568.
- Ayodele, T. O. (2010). Machine learning overview, Intech Open Access Publisher.
- Sebastiani, F. (2002). Machine learning in automated text categorization, ACM computing surveys (CSUR), 34, 1-47. Nitze, I., Schulthess, U., Asche, H. (2012). Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th. GEOBIA, 35-40.
- Osowski, S., Siwekand, K., Markiewicz, T. (2004). MLP and SVM Networks – a Comparative Study Proceedings of the 6th Nordic Signal Processing Symposium – NORSIG.
- Soman, K.P., Loganathan, R., Ajay, V. (2011). Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., 486.
- Freedman, D. A. (2009). Statistical Models, Cambridge University Press.
- Tsang A.H.C. (2002). Strategic Dimensions of Maintenance Management, Journal of Quality in Maintenance Engineering, 8, 7-39.
- Lyonnet, P. (1991). Maintenance Planning: Methods and Mathematics, Chapman&Hall,
- Cassady C.R., Pohl, E.A., Murdock W.P. (2001). Selective Maintenance Modeling for Industrial Systems, Journal of Quality in Maintenance Engineering, 7, 104-117.
- Baraçlı, H., Coşkun, S., Eser, A. (2001). Toplam kalite programlarının başarılı olarak uygulanabilmesinde toplam üretken bakım tekniği, I. Demir-Çelik Sempozyumu, 340-341, Zonguldak.
- Swanson, L. (2001). Linking Maintenance Strategies to Performance, Int. J. Production Economics, 70, 237-244.
- Worsham, C.W. (2004). “Önleyici bakım gerekli midir?”, Mühendis ve Makine Dergisi, 45, Sayı 538, 21-23.
- Kenne, J.P., Nkeungoue, L.J. (2007). Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system, Applied Numerical Mathematics, doi:10.1016/j.apnum.2006.11.010.
- Zhou, X., Xi, L., Lee, J. (2007). Reliability centered predictive maintenance scheduling for a continuously monitored system subject to degradation, Reliability Engineering & System Safety, 92, 530-534.
- Rohani, A., Abbaspour-Fard, M., Abdolahpour, S. (2011). Prediction of tractor repair and maintenance costs using Artificial Neural Network, Expert Syst. Appl., 38, 8999–9007.
- Tamilselvan, P., Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 115, 124–135.
- Öztanır, O. (2018). “Makine öğrenmesi kullanılarak kestirimci bakım”, (Yüksek lisans tezi), openaccess.hacettepe.edu.tr.
- Konar, P., Chattopadhyay, P. (2011). “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Appl. Soft Comput. J., 11, 4203–4211.
Abstract
While maintenance is considered a necessary activity in every production establishment, it is defined as a critical business function that
affects the income and expenses of the company today. The concept of machine learning is the phenomenon of developing algorithms that enable
machines to make better decisions by educating themselves in the face of the situations they encounter. Machine learning techniques have been used
in maintenance planning estimation studies since the 1950s. In this study conducted by a defense industry firm, the problem of the fact that
the parts with high costs due to sudden and unplanned maintenance of the machines are scrap and the company pays a high amount of fines to
customers due to delays in shipments. The purpose of this study is to predict the maintenance planning and malfunctions to be made by using
developing science and technology, to minimize the cost loss and completely prevent stopping in production, in addition, the supervised
learning technique, one of the Machine Learning techniques, was applied in the most critical chemical paint machine in the defense industry
company, and maintenance planning was estimated.
References
- Vapnik, V. (2000). The Nature of Statistical Learning Theory, Second Ed., 1, New York.
- Nils J. Nilsson, (1996). Introduction to Machine Learning: An Early Draft of a Proposed Textbook, Stanford University.
- Nourmohammadzadeh, A., Hartmann, S. (2015). Fault Classification of a Centrifugal Pump in Normal and Noisy Environment with Artificial Neural Network and Support Vector Machine Enhanced by a Genetic Algorithm in Theory and Practice of Natural Computing. Lecture Notes in Computer Science.
- Hashemian, H., Bean, W. C. (2011). “State-of-the-art predictive maintenance techniques,” IEEE Trans. Instrum. Meas., 60, 3480–3492
- Jahnke, P. (2015). Machine Learning Approaches for Failure Type Detection and Predictive Maintenance, 83.
- Garcia, M. A., Sanz-Bobi, Pico, J. (2006). SIMAP: Intelligent System for Predictive Maintenance. “Application to the health condition monitoring of a wind turbine gearbox”, Comput. Ind., 57, 552–568.
- Ayodele, T. O. (2010). Machine learning overview, Intech Open Access Publisher.
- Sebastiani, F. (2002). Machine learning in automated text categorization, ACM computing surveys (CSUR), 34, 1-47. Nitze, I., Schulthess, U., Asche, H. (2012). Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th. GEOBIA, 35-40.
- Osowski, S., Siwekand, K., Markiewicz, T. (2004). MLP and SVM Networks – a Comparative Study Proceedings of the 6th Nordic Signal Processing Symposium – NORSIG.
- Soman, K.P., Loganathan, R., Ajay, V. (2011). Machine learning with SVM and other kernel methods. PHI Learning Pvt. Ltd., 486.
- Freedman, D. A. (2009). Statistical Models, Cambridge University Press.
- Tsang A.H.C. (2002). Strategic Dimensions of Maintenance Management, Journal of Quality in Maintenance Engineering, 8, 7-39.
- Lyonnet, P. (1991). Maintenance Planning: Methods and Mathematics, Chapman&Hall,
- Cassady C.R., Pohl, E.A., Murdock W.P. (2001). Selective Maintenance Modeling for Industrial Systems, Journal of Quality in Maintenance Engineering, 7, 104-117.
- Baraçlı, H., Coşkun, S., Eser, A. (2001). Toplam kalite programlarının başarılı olarak uygulanabilmesinde toplam üretken bakım tekniği, I. Demir-Çelik Sempozyumu, 340-341, Zonguldak.
- Swanson, L. (2001). Linking Maintenance Strategies to Performance, Int. J. Production Economics, 70, 237-244.
- Worsham, C.W. (2004). “Önleyici bakım gerekli midir?”, Mühendis ve Makine Dergisi, 45, Sayı 538, 21-23.
- Kenne, J.P., Nkeungoue, L.J. (2007). Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system, Applied Numerical Mathematics, doi:10.1016/j.apnum.2006.11.010.
- Zhou, X., Xi, L., Lee, J. (2007). Reliability centered predictive maintenance scheduling for a continuously monitored system subject to degradation, Reliability Engineering & System Safety, 92, 530-534.
- Rohani, A., Abbaspour-Fard, M., Abdolahpour, S. (2011). Prediction of tractor repair and maintenance costs using Artificial Neural Network, Expert Syst. Appl., 38, 8999–9007.
- Tamilselvan, P., Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 115, 124–135.
- Öztanır, O. (2018). “Makine öğrenmesi kullanılarak kestirimci bakım”, (Yüksek lisans tezi), openaccess.hacettepe.edu.tr.
- Konar, P., Chattopadhyay, P. (2011). “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Appl. Soft Comput. J., 11, 4203–4211.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Industrial Engineering |
| Journal Section | Research Article |
| Authors | |
| Publication Date | June 30, 2021 |
| Submission Date | April 2, 2021 |
| Acceptance Date | May 18, 2021 |
| Published in Issue | Year 2021 Volume: 5 Issue: 1 |
Cite
