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Demiryolu Hatları için Akıllı Teşhis ve Bakım Sistemleri

Yıl 2021, Cilt: 4 Sayı: 2, 134 - 147, 29.11.2021
https://doi.org/10.51513/jitsa.951322

Öz

Son yıllarda yapay zeka (AI), nesnelerin interneti (IoT) ve büyük veri gibi ileri teknolojiler ön plana çıkmaktadır. Bu teknolojiler çeşitli sektörlerde geniş bir kullanım alanına sahiptir. İnsan ve yük taşımacılığının önemli bir parçası olan demiryolu sistemleri, bu yeni teknolojilerin entegrasyonu ile iyileştirilmelidir. Hat arızalarının başarılı bir şekilde tespiti ve bu tespitlere göre yapılan hat bakımları, demiryolu işletmesinin emniyeti için gereklidir. Şu anda, görüntü işleme ve makine öğrenimi yardımıyla örüntü tanıma uygulamaları, otomatik hat denetimleri için yaygın olarak kullanılmaktadır. Ancak demiryolu hatlarının günümüz teknolojisiyle mükemmel bir şekilde entegre olduğunu söylemek mümkün değildir. Bu çalışmada, geleneksel ve akıllı teşhis ve bakım yöntemleri arasındaki farklara yer verilmiştir. İleri teknolojilerin demiryolu hatlarına uygulanmasındaki eksiklikler tespit edilmiş ve daha iyi bir gelişim için gerekli eylemler tartışılmıştır. Son olarak, akıllı sistemlerin kullanımının, yapıların yaşam döngüsü üzerindeki etkileri değerlendirilmiştir.

Kaynakça

  • Andre, L. O. De Melo, Kaewunruen S., Papaelias M., Liedi L.B. Bernucci, Motta R. (2020). Methods to Monitor and Evaluate the Deterioration of Track and Its Components in a Railway In-Service: A Systemic Review. Frontiers in Built Environment, 6, Article 118.
  • APTA. (2017). Ridership report – quarterly and annual totals by mode. Retrieved January 12, 2021 from www.apta.com/resources/statistics/Pages/ridershipreport.aspx.
  • Asplund, M. 2016. Wayside condition monitoring technologies for railway systems. PhD thesis. Sweden: Lulea University.
  • AREMA (American Railway Engineering and Maintenance-of-Way Association). (2016). Manual for railway engineering, Vol. 4, Lanham,MD.
  • Attoh-Okine, N. (2014). Big data challenges in railway engineering. In 2014 IEEE International Conference on Big Data.
  • Balci, E. (2019). Overview of Intelligent Personal Assistants, Acta Infologica, 3(1), 22-33.
  • Snijders, C., Matzat, U., Reips, U.-D. (2012). Big data: Big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7(1), 1-5.
  • Li, C., Luo, S., Cole, C., Spiryagin, M. (2017). An overview: modern techniques for railway vehicle on-board health monitoring systems. Vehicle System Dynamics, 55(7), 1045-1070.
  • EN 13848-5:2008+A1:2010 railway applications – track – track geometry quality. (2008). – Part 5: geometric quality levels-Plain line
  • Faghih-Roohi, S., Hajizadeh, S., Nunez, A., Babuska, R., De Schutter, B. (2016). Deep convolutional neural networks for detection of rail surface defects. In Proceedings of International Joint Conference on Neural Networks, 2584-2589.
  • Hayashi, Y., Kojima, T., Tsunashima, H., Marumo, Y. (2006). Real time fault detection of railway vehicle and tracks. In International conference on railway condition monitoring, Birmingham, UK, 20-25.
  • Jamshidi, A., Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., Dollevoet, R., Li, Z., De Schutter, B. (2017). A Big Data Analysis Approach for Rail Failure Risk Assessment. Risk Analysis, 37, 1495-1507.
  • Jamshidi, A., Hajizadeh, S., Su, Z., Naeimi, M., Núñez, A., Dollevoet, R., Schutter, B. De, Li, Z. (2018). A decision support approach for condition-based maintenance of rails based on big data analysis. Transportation Research Part C: Emerging Technologies, 95, 185-206.
  • Kugurakova, V., Talanov, M., Manakhov, N., Ivanov, D. (2015). Anthropomorphic Artificial Social Agent with Simulated Emotions and its Implementation. Procedia Computer Science, 71, 112-118.
  • Tan, L., Wang, N. (2010). Future internet: The Internet of Things. In 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 5., 376-380.
  • Marschnig, S. (2016). Innovative track access charges. Transportation Research Procedia, 14, 1884-1893.
  • Mohanty, S. P., Choppali, U., Kougianos, E. (2016). Everything you wanted to know about smart cities: The Internet of things is the backbone. IEEE Consumer Electronics Magazine, 5(3), 60-70.
  • Nakhaee M. C., Hiemstra D., Stoelinga M., Noort M.V. (2019). The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey. Springer Nature Switzerland, RRSRail., 91-105.
  • Neuhold, J., Landgraf, M., Marschnig, S., Veit, P. (2020). Measurement Data-Driven Life-Cycle Management of Railway Track. Transportation Research Record, 2674(11), 685-696.
  • ORR. (2017). Network rail monitor – quarters 3-4 of year 3 of CP5 16-17 (London)
  • Peng, F. (2011). Scheduling of track inspection and maintenance activities in railroad networks. PhD Dissertation, University of Illinois at Urbana-Champaign
  • Santur, Y., Karaköse, M., Akın, E. (2016). Condition Monitoring Approach Using 3D- Modelling of Railway Tracks with Laser Cameras. In International Conference on Advanced Technology & Sciences (ICAT’16), 132-135.
  • Sasidharan, M., Burrow, M. P. N., Ghataora, G. S. (2020). A whole life cycle approach under uncertainty for economically justifiable ballasted railway track maintenance. Research in Transportation Economics, 100815.
  • Song, B. Y., Zhong, Y., Liu, R. K., Wang, F. T. (2014). Railway Maintenance Analysis Based on Big Data and Condition Classification. Advanced Materials Research, 919-921,1134-1138.
  • Van Noortwijk, J. M. (2004). Gamma process model for time-dependent structural reliability analysis. In Numerical Modelling of Discrete Materials in Geotechnical Engineering, Civil Engineering and Earth Sciences: Proceedings of the First International UDEC/3DEC Symposium, Bochum, Germany, 29 September-1 October 2004 (p. 101). CRC Press.
  • Veit, P. (2007). Track Quality—Luxury or Necessity?. Railway Technical Review—RTR Special, 8-12.
  • Yokoyama, A. (2015). Innovative changes for maintenance of railway by using ICT -To Achieve “Smart Maintenance”. Procedia CIRP, 24-29.
  • Zoeteman, A., Dollevoet, R., Li, Z. (2014). Dutch research results on wheel/rail interface management: 2001-2013 and beyond. Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit, 228, 642-651.

Smart Diagnosis And Maintenance Systems For Railway Tracks

Yıl 2021, Cilt: 4 Sayı: 2, 134 - 147, 29.11.2021
https://doi.org/10.51513/jitsa.951322

Öz

In recent years, advanced technologies such as artificial intelligence (AI), the internet of things (IoT), and big data came into prominence. These technologies found an extensive area of utilization in various sectors. Railway systems as an important part of the transportation of people and goods should be improved by the integration of novel technologies. Successful detection of track faults and operating maintenance tasks accordingly are essential for the safety of railway operations. Currently, image processing and pattern recognition via machine learning applications are in common use for automated track inspections. However, it is not possible to claim that railway tracks are integrated with current technology perfectly. In this work, differences between the traditional way and the smart way of track inspection and maintenance are presented. Shortcomings of the application of advanced technologies into railway tracks are detected and required actions for further improvements are discussed. Lastly, the effects of the use of smart systems on the life cycle of the structures are evaluated.

Kaynakça

  • Andre, L. O. De Melo, Kaewunruen S., Papaelias M., Liedi L.B. Bernucci, Motta R. (2020). Methods to Monitor and Evaluate the Deterioration of Track and Its Components in a Railway In-Service: A Systemic Review. Frontiers in Built Environment, 6, Article 118.
  • APTA. (2017). Ridership report – quarterly and annual totals by mode. Retrieved January 12, 2021 from www.apta.com/resources/statistics/Pages/ridershipreport.aspx.
  • Asplund, M. 2016. Wayside condition monitoring technologies for railway systems. PhD thesis. Sweden: Lulea University.
  • AREMA (American Railway Engineering and Maintenance-of-Way Association). (2016). Manual for railway engineering, Vol. 4, Lanham,MD.
  • Attoh-Okine, N. (2014). Big data challenges in railway engineering. In 2014 IEEE International Conference on Big Data.
  • Balci, E. (2019). Overview of Intelligent Personal Assistants, Acta Infologica, 3(1), 22-33.
  • Snijders, C., Matzat, U., Reips, U.-D. (2012). Big data: Big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7(1), 1-5.
  • Li, C., Luo, S., Cole, C., Spiryagin, M. (2017). An overview: modern techniques for railway vehicle on-board health monitoring systems. Vehicle System Dynamics, 55(7), 1045-1070.
  • EN 13848-5:2008+A1:2010 railway applications – track – track geometry quality. (2008). – Part 5: geometric quality levels-Plain line
  • Faghih-Roohi, S., Hajizadeh, S., Nunez, A., Babuska, R., De Schutter, B. (2016). Deep convolutional neural networks for detection of rail surface defects. In Proceedings of International Joint Conference on Neural Networks, 2584-2589.
  • Hayashi, Y., Kojima, T., Tsunashima, H., Marumo, Y. (2006). Real time fault detection of railway vehicle and tracks. In International conference on railway condition monitoring, Birmingham, UK, 20-25.
  • Jamshidi, A., Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., Dollevoet, R., Li, Z., De Schutter, B. (2017). A Big Data Analysis Approach for Rail Failure Risk Assessment. Risk Analysis, 37, 1495-1507.
  • Jamshidi, A., Hajizadeh, S., Su, Z., Naeimi, M., Núñez, A., Dollevoet, R., Schutter, B. De, Li, Z. (2018). A decision support approach for condition-based maintenance of rails based on big data analysis. Transportation Research Part C: Emerging Technologies, 95, 185-206.
  • Kugurakova, V., Talanov, M., Manakhov, N., Ivanov, D. (2015). Anthropomorphic Artificial Social Agent with Simulated Emotions and its Implementation. Procedia Computer Science, 71, 112-118.
  • Tan, L., Wang, N. (2010). Future internet: The Internet of Things. In 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 5., 376-380.
  • Marschnig, S. (2016). Innovative track access charges. Transportation Research Procedia, 14, 1884-1893.
  • Mohanty, S. P., Choppali, U., Kougianos, E. (2016). Everything you wanted to know about smart cities: The Internet of things is the backbone. IEEE Consumer Electronics Magazine, 5(3), 60-70.
  • Nakhaee M. C., Hiemstra D., Stoelinga M., Noort M.V. (2019). The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey. Springer Nature Switzerland, RRSRail., 91-105.
  • Neuhold, J., Landgraf, M., Marschnig, S., Veit, P. (2020). Measurement Data-Driven Life-Cycle Management of Railway Track. Transportation Research Record, 2674(11), 685-696.
  • ORR. (2017). Network rail monitor – quarters 3-4 of year 3 of CP5 16-17 (London)
  • Peng, F. (2011). Scheduling of track inspection and maintenance activities in railroad networks. PhD Dissertation, University of Illinois at Urbana-Champaign
  • Santur, Y., Karaköse, M., Akın, E. (2016). Condition Monitoring Approach Using 3D- Modelling of Railway Tracks with Laser Cameras. In International Conference on Advanced Technology & Sciences (ICAT’16), 132-135.
  • Sasidharan, M., Burrow, M. P. N., Ghataora, G. S. (2020). A whole life cycle approach under uncertainty for economically justifiable ballasted railway track maintenance. Research in Transportation Economics, 100815.
  • Song, B. Y., Zhong, Y., Liu, R. K., Wang, F. T. (2014). Railway Maintenance Analysis Based on Big Data and Condition Classification. Advanced Materials Research, 919-921,1134-1138.
  • Van Noortwijk, J. M. (2004). Gamma process model for time-dependent structural reliability analysis. In Numerical Modelling of Discrete Materials in Geotechnical Engineering, Civil Engineering and Earth Sciences: Proceedings of the First International UDEC/3DEC Symposium, Bochum, Germany, 29 September-1 October 2004 (p. 101). CRC Press.
  • Veit, P. (2007). Track Quality—Luxury or Necessity?. Railway Technical Review—RTR Special, 8-12.
  • Yokoyama, A. (2015). Innovative changes for maintenance of railway by using ICT -To Achieve “Smart Maintenance”. Procedia CIRP, 24-29.
  • Zoeteman, A., Dollevoet, R., Li, Z. (2014). Dutch research results on wheel/rail interface management: 2001-2013 and beyond. Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit, 228, 642-651.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Erdem Balcı 0000-0003-1759-1946

Ertan Yalçın 0000-0001-5925-3131

Tunay Uzbay Yelce 0000-0001-9965-4271

Niyazi Bezgin 0000-0002-6518-0378

Yayımlanma Tarihi 29 Kasım 2021
Gönderilme Tarihi 12 Haziran 2021
Kabul Tarihi 20 Eylül 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 4 Sayı: 2

Kaynak Göster

APA Balcı, E., Yalçın, E., Yelce, T. U., Bezgin, N. (2021). Smart Diagnosis And Maintenance Systems For Railway Tracks. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 4(2), 134-147. https://doi.org/10.51513/jitsa.951322