Prediction of Radio Signal Failures of Communication Based Train Operating Systems by Machine Learning Methods

Yıl 2024, Cilt: 17 Sayı: 1, 1 - 15, 28.03.2024

Burak Arslan , Hasan Tiryaki

https://doi.org/10.18185/erzifbed.1196965

Öz

The use of rail systems in urban public transportation has become a necessity for reasons such as time saving, travel comfort and operating costs, especially in cities with high population and road traffic. Communication based train operating systems (CBTC) are used for the safe use of urban rail systems and the maximum capacity of the railway line. In this study, in line with the data collected from the trains on a railway line operated with CBTC, the status of the radio signals that enable the wireless communication of the trains with the trackside signaling equipment was evaluated by machine learning methods, and the situations that may have negative effects on the train operations of the problems at the signal level were evaluate. The problems on the antennas which receives signals from trackside above trains, the poor connection related with fiber optical and LAN cables, the trackside transmitter antenna orientation problems causes decrease on signal levels. It is aimed that to take actions about the problematic signal levels without any negative impact on the passenger comfort and the operation yet. The radio signal losses cause unexpected trains stops and delays. A decision support model has been developed that will offer early solution suggestions to system maintainers in order to intervene first. In conclusion, since it is the first study related with failure prediction by using radio signal levels data on railway signaling system, this study presents an important innovation in terms of literature.

Anahtar Kelimeler

Railway transportation, communication based train operating systems, machine learning methods, failure prediction, maintenance management

Makine Öğrenmesi Yöntemleri ile Haberleşme Tabanlı Tren İşletim Sistemlerinin Radyo Sinyal Hatalarının Tahmini

Öz

Kentsel toplu taşımada raylı sistemlerin kullanılması, özellikle nüfus ve karayolu trafiğinin yoğun olduğu şehirlerde zaman tasarrufu, seyahat konforu ve işletme maliyetleri gibi nedenlerle bir zorunluluk haline gelmiştir. Kent içi raylı sistemlerin güvenli kullanımı ve demiryolu hattının maksimum kapasiteyle kullanımı için haberleşme tabanlı tren işletim sistemleri (CBTC) kullanılmaktadır. Bu çalışmada, CBTC ile işletilen bir demiryolu hattındaki trenlerden toplanan veriler doğrultusunda, trenlerin yol kenarı sinyalizasyon ekipmanları ile kablosuz iletişimini sağlayan sinyallerinin durumu makine öğrenmesi yöntemleri ile değerlendirilmiş ve durumlar değerlendirilmiştir. Tren üzerinde radyo sinyallerini yakalayan antenlerin bağlantılarında, hat boyu verici radioların fiber optic ve LAN kablo sonlandırmalarında, hat boyu verici antenlerin oryantasyonlarındaki problemler sinyal seviyelerinde düşmelere sebep olmaktadır. Sinyal seviyesindeki problemlerin tren işletmesini olumsuz etkileyebileceği durumlar değerlendirilmiş, yolcu konforuna ve operasyonuna henüz olumsuz bir etkisi olmadan müdahale edilmesi amaçlanmıştır. Radyo sinyal seviyelerindeki kayıplar beklenmedik tren duruşlarına ve tehirlere sebep olmaktadır. Sistem yöneticilerine önceden müdahale etmeleri için erken çözüm önerileri sunacak bir karar destek modeli geliştirilmiştir. Sonuç olarak, demiryolu sinyalizasyon sistemindeki radio sinyal seviyeleri verileri kullanılarak arıza tahmini ile ilgili ilk çalışma olması nedeniyle bu çalışma literatür açısından önemli bir yenilik sunmaktadır.

Anahtar Kelimeler

Demiryolu ulaşımı, haberleşme tabanlı tren işletim sistemleri, makine öğrenmesi yöntemleri, hata tahmini, bakım yönetimi

Kaynakça

• [1] Lee, J., et al., (2016), “Fault detection and diagnosis of railway point machines by sound analysis”, Sensors, vol. 16, no. 4, pp. 549-561. doi: 10.3390/s16040549
• [2] Vileniskis, M., Remenyte Prescott, R., Rama, D., (2015), “A fault detection method for railway point systems”, Proceedings of IMechE Part F: J Rail and Rapid Transit vol. 230, no. 3, pp. 852-865. doi: 10.1177/0954409714567487
• [3] Arakani, H., et al., (2012), “PHM for railway system- a case study on health assessment of the point machines”, in IEEE Conference on Prognostics and Health Management (PHM), Denver CO, USA, pp. 1-5.
• [4] Bemment, S.D., Goodall, R.M., Dixon, R., Ward, C.P., (2017), “Improving the reliability and availability of railway track switching by analysing historical failure data and introducing functionally redundant subsystems”, Proceedings of IMechE Part F: J Rail and Rapid Transit, vol. 232, no. 5, pp. 1407-1424. doi: 10.1177/0954409717727879
• [5] Vapnik, V., Izmailov, R., (2017), “Knowledge transfer in SVM and neural networks”, Annals of Mathematics and Artificial Intelligence, vol. 81, no. 2017, pp. 3-19. doi: 10.1007/s10472-017-9538-x
• [6] Grobbelaar, S., Visser, J.K., (2015), “Determining the cost of predictive component replacement in order to assist with maintenance decision-making”, South African Journal of Industrial Engineering, vol. 26, no. 1, pp. 150-162. doi: 10.7166/26-1-713
• [7] Eker, O.F., Camci, F., Kumar, U., (2012), “SVM based diagnostics on railway turnouts”, International Journal of Performability Engineering, vol. 8, no. 3, pp. 289-298. doi: 10.23940/ijpe.12.3.p289.mag
• [8] Molina, L., et al., (2011), “Condition monitoring of railway turnouts and other track components using machine vision”, in Transportation Research Board 90th Annual Meeting, Washington DC, USA, pp. 1-17.
• [9] Arslan, B., Tiryaki, H., (2020), “Prediction of railway switch point failures by artificial intelligence methods”, Turkish Journal of Electrical Engineering and Computer Science, vol. 28, no. 2, pp. 1044-1058. doi: 10.3906/elk-1906-66
• [10] Cinus, M., Confalonieri, M., Barni, A., Valente, A., (2016), “An ANN based decision support system fostering production plan optimization through preventive maintenance management”, in Advances in neural networks, Springer, Cham.
• [11] Amruthnath, N., Gupta, T., (2018), “A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance”, in 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, pp. 355-361.
• [12] Krenek, J., Kuca, K., Blazek, P., Krejcar, O., Jun, D., (2016), “Application of artificial neural networks in condition based predictive maintenance”, in Recent developments in intelligent information and database systems. Studies in computational intelligence, Springer, Cham.
• [13] Sun, F., Gao, L., Zou, J., Wu, T., Li, J., (2013), “Study on multi-equipment failure prediction based on system network”, Sensors & Transducers, vol. 158, no. 11, pp. 427-435.
• [14] Jančíková, Z., Zimný, O., Koštial, P., (2013), “Prediction of metal corrosion by neural networks”, Metalurgija, vol. 52, no. 3, pp. 379-381.
• [15] Xu, J.K., Chen, L.J., Gao, W.M., Zhao, M.J., (2015), “CBTC simulation platform design and study”, Journal of Computer and Communications, vol. 3, no. 2015, pp. 61-67. doi: 10.4236/jcc.2015.39007
• [16] Oztemel, E., (2012), Artificial neural networks, Papatya Publishing, Türkiye.
• [17] Sharma, V., Rai, S., Dev, A., (2012), “A comprehensive study of artificial neural networks”, International Journal of Advanced Research in Computer Science and Software Search, vol. 2, no. 10, pp. 278-284. doi: 10.1.1.468.9353
• [18] Cuhadar, M., (2006), “Use of artificial neural networks for demand forecasting in tourism sector and comparative analysis with other methods”, Ph.D. dissertation, Social Sciences Institute, Suleyman Demirel Univ., Isparta, Türkiye.
• [19] Maind, S.B., Wankar, P., (2014), “Research paper on basic of artificial neural network”, International Journal on Recent and Innovation Trends in Computing and Communication, vol. 2, no. 1, pp. 96-100.
• [20] Akcay, M.T., Akgundogdu, A., Tiryaki, H., (2021), “Estimation of the average speed for a railway signaling system by using gaussian process regression methods with bayesian optimization”, Railway Engineering, no. 14, pp. 274-286. doi: 10.47072/demiryolu.942730
• [21] Akcay, M.T., Akgundogdu, A., Tiryaki, H., (2022), “Prediction of travel time for railway traffic management by using the adaboost algorithm,” Journal of Balikesir University Institute of Natural and Applied Sciences, vol. 24, no. 1, pp. 300-312. doi: 10.25092/baunfbed.937333