Predictive Maintenance Based On Machine Learning In Public Transportation Vehicles

Öz

Predictive maintenance is an approach to prevent failure in a system by estimating the time of failure before a mechanical component fails, so that the maintenance decision can be properly planned. In the public transport industry, whose efficiency is heavily dependent on equipment, anticipating breakdowns is vital. In this study, predictive maintenance work was carried out in order to minimize problems such as malfunctions in public transport vehicles, stopping the voyage, delaying the journey and causing an accident due to unplanned breakdowns. Based on instant vehicle health data obtained from IoT sensors, classification techniques were run in machine learning. For maintenance planning, the probability of vehicles being normal and malfunctioning was examined with fuzzy logic and fuzzy outputs were obtained at maintenance speed. With the predictive maintenance approach applied to the data of the study taken from the vehicles, almost all of the faults in the vehicles could be detected.

Anahtar Kelimeler

• Maintenance
• Predictive Maintenance
• Machine Learning
• Internet of Things
• Smart Public Transportation

Toplu Taşıma Araçlarında Makine Öğrenmesine Dayalı Kestirimci Bakım

Öz

Kestirimci bakım, bir sistemin mekanik bir bileşeninde arıza oluşmadan önce arıza zamanını tahmin ederek bakım kararının doğru planlanabilmesi için arızanın önlenmesini sağlayan bir yaklaşımdır. Verimliliği büyük ölçüde donanıma bağlı olan toplu taşıma sektöründe, arızaların önceden tahmin edilmesi hayati önem taşır. Bu çalışmada toplu taşıma araçlarında meydana gelen arıza, seferin durdurulması, yolculuğun ertelenmesi ve plansız arızalar nedeniyle kazaya neden olması gibi sorunları en aza indirmek için kestirimci bakım çalışması yapılmıştır. IoT sensörlerinden elde edilen anlık araç sağlığı verilerine dayalı olarak makine öğrenmesinde sınıflandırma teknikleri çalıştırılmıştır. Bakım planlaması için araçların normal ve arızalı olma olasılığı bulanık mantıkla incelenmiş ve bakım hızında bulanık çıktılar elde edilmiştir. Araçlardan alınan çalışmanın verilerine uygulanan kestirimci bakım yaklaşımı ile araçlardaki arızaların neredeyse tümü tespit edilebilmiştir.

Anahtar Kelimeler

• Bakım
• Kestirimci Bakım
• Makine Öğrenmesi
• Nesnelerin İnterneti
• Akıllı Toplu Taşıma Sistemleri

Kaynakça

1. Binding, A., Dykeman, N., Pang, S. 2019. “Machine Learning Predictive Maintenance on Data in the Wild”, In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 507-512, IEEE.
2. Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp, 16, 3-8.
3. Dunn, S. (2002). Maintenance terminology-some key terms. Plant Maintenance Resource Center.
4. Bengtsson, M. (2004). Condition based maintenance system technology–Where is development heading. Condition Based Maintenance Systems–An Investigation of Technical Constituents and Organizational Aspects, 55.
5. Carnero, M. C. (2005). Selection of diagnostic techniques and instrumentation in a predictive maintenance program. A case study. Decision support systems, 38(4), 539-555.
6. J.W. Weyerhaeuser, Bearing Failures Dry Up at Weyerhaeuser, Practicing Oil Analysis, J. Fitch, Tulsa, 2000 (March – April).
7. A.H. Christer, W. Wang, J. M. Sharp, A state space condition monitoring model for furnace erosion prediction and replacement, European Journal of Operational Research 101 (1997) 1 – 14.
8. M. Lupinucci, J.G. Per'rez Davila, L. Tiseyra, Improving sheet 554 M.C. Carnero / Decision Support Systems 38 (2005) 539–555 metal quality and producto throughput with bently's machinery management system vol. 21, no. 3, Orbit, Bently, NV, 2000.

9. P. Beltra´n, A. Lo´pez, El Mantenimiento Predictivo en aerogeneradores. Caso pra´ctico: estudio de averı´as, Proceedings 4jCongreso Espan˜ol de Mantenimiento, AEM, Barcelona, 2000.
10. J.M. Villar, L.O. Masson, J.A. Gomes, Proactive maintenance—a successful history vol. 21 no. 3, Orbit, Bently, NV, 2000.
11. V. Kakkar, Ontario power generation’s nanticoke power plant vol. 20, no. 4, Orbit, Bently, NV, 1999.
12. F. Barbera, H. Schneider, E. Watson, A condition based maintenance model for two-unit series system, European Journal of Operational Research 116 (1999) 281 – 290.
13. Carnero, M. 2006. “An evaluation system of the setting up of predictive maintenance programmes”, Reliability Engineering & System Safety, 91(8), 945-963.
14. 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.
15. Karakose, M., Yaman, O. 2020. “Complex fuzzy system based predictive maintenance approach in railways”, IEEE Transactions on Industrial Informatics, 16(9), 6023-6032.
16. Shamayleh, A., Awad, M., & Farhat, J. 2020. “IoT based predictive maintenance management of medical equipment”, Journal of medical systems, 44(4), 1-12.
17. Sipos, R., Fradkin, D., Moerchen, F., Wang, Z. 2014. “Log-based predictive maintenance”, In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining,1867-1876.
18. Traini, E., Bruno, G., D’antonio, G., Lombardi, F. 2019. “Machine learning framework for predictive maintenance in milling”, IFAC-PapersOnLine, 52(13), 177-182.
19. Zhang, W., Yang, D., Wang, H. 2019. “Data-driven methods for predictive maintenance of industrial equipment: A survey”, IEEE Systems Journal, 13(3), 2213-2227.
20. Grall, A., Dieulle, L., Bérenguer, C., Roussignol, M. 2002. “Continuous-time predictive-maintenance scheduling for a deteriorating system”, IEEE transactions on reliability, 51(2), 141-150.
21. Mobley, R. K. 2002. “An introduction to predictive maintenance”, Elsevier.
22. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.
23. Gunn, S. R. (1998). Support vector machines for classification and regression. ISIS technical report, 14(1), 5-16.
24. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
25. Seeja, K. R., & Zareapoor, M. (2014). Fraudminer: A novel credit card fraud detection model based on frequent itemset mining. The Scientific World Journal, 2014.
26. Ryali, S., Supekar, K., Abrams, D. A., & Menon, V. (2010). Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage, 51(2), 752-764.
27. Kurt, I., Ture, M., & Kurum, A. T. (2008). Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert systems with applications, 34(1), 366-374.
28. Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine learning, 29(2), 103-130.
29. Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003, November). KNN model-based approach in classification. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 986-996). Springer, Berlin, Heidelberg.
30. Sammut, C., & Webb, G. I. (Eds.). (2011). Encyclopedia of machine learning. Springer Science & Business Media.
31. Lewis, H. G., & Brown, M. (2001). A generalized confusion matrix for assessing area estimates from remotely sensed data. International journal of remote sensing, 22(16), 3223-3235.
32. Kosko, B., & Isaka, S. (1993). Fuzzy logic. Scientific American, 269(1), 76-81.
33. Zadeh, L. A. (1988). Fuzzy logic. Computer, 21(4), 83-93.
34. Zhao, J., & Bose, B. K. (2002, November). Evaluation of membership functions for fuzzy logic controlled induction motor drive. In IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02 (Vol. 1, pp. 229-234). IEEE.