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Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı

Year 2023, Volume: 13 Issue: 1, 13 - 22, 23.01.2023

Abstract

Günümüzde demiryolu sektöründe tren taşımacılığının önemi göz önüne alındığında endüstriyel Peron Ayırıcı Kapı Sistemi (PAKS) genellikle emniyet açısından kritik sistemler olarak tanımlanmaktadır. PAKS sisteminde meydana gelebilecek arızalar, tren ulaşımının elverişliliğini ciddi şekilde etkileyecektir. PAKS sisteminin emniyetli ve güvenilir bir şekilde çalışmasını sağlamak için bu çalışmada PAKS sistemi üzerinde veriye dayalı hata tespit ve sınıflandırma yöntemi çalışılmıştır. Bu yöntemde yapay sinir ağı (YSA) güçlü kabiliyetleri nedeniyle tercih edilmiştir. PAKS sistemi üzerinde yapay olarak farklı çalışma durumları (sağlıklı ve hatalı) oluşturulmuş ve kapının çalışmasını sağlayan elektriksel motor akımı ve gerilimi sinyalleri veri seti olarak kullanılmıştır. Toplamda beş farklı çalışma durumu için her biri 1000 açma/kapama döngüsü üzerinden veriler toplanmış ve her bir durum için 12 adet öz nitelik değerleri hesaplanarak YSA modelleri için giriş/çıkış veri setleri elde edilmiştir. Dört farklı YSA yapısı üzerinde çalışılmış ve eğitme/test aşamaları bu yapılara uygulanmıştır. Doğruluk, hassasiyet, kesinlik, belirginlik gibi performans parametreleri bu YSA modelleri için karşılaştırmalı olarak hesaplanmıştır. Elde edilen sonuçlara göre, toplamda üç katmanlı (giriş-gizli katman-çıkış) ve nöron sayıları sırasıyla 12-7-5 olan yapının en iyi performansı gösterdiği gösterilmiştir. Sonuç olarak YSA yapısının PAKS sistemi üzerindeki oluşabilecek hataları teşhis etmede kullanışlı bir AI aracı olduğu gözlemlenmiştir.

Supporting Institution

Albayrak Makine Elektronik A.Ş., Eskişehir

Thanks

Bu çalışma, ECOMAI PENTA-EURIPIDES (Hibe No. 2021028) çatı projesi kapsamında TÜBİTAK (Hibe No. 9210043) tarafından desteklenmiştir.

References

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  • [34] T. Ince, S. Kiranyaz, L. Eren, M. Askar, ve M. Gabbouj, “Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks”, IEEE Trans. Ind. Electron., c. 63, sy 11, ss. 7067-7075, Kas. 2016, doi: 10.1109/TIE.2016.2582729.
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  • [39] I. N. da Silva, R. Andrade Flauzino, S. F. dos Reis Alves, D. Hernane Spatti, ve L. H. B. Liboni, Artificial Neural Networks: A Practical Course, 1st ed. 2017. Cham: Springer International Publishing : Imprint: Springer, 2017. doi: 10.1007/978-3-319-43162-8.
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  • [42] Z. Comert, A.F. Kocamaz, “A study of artificial neural network training algorithms for classification of cardiotocography signals,” Bitlis Eren University Journal Of Science And Technology vol. 7(2), ss 93–103, 2017.
  • [43] Y. Liu, J. Starzyk, Z. Zhu, “Optimizing number of hidden neurons in neural networks.. IASTED International Conference on Artificial Intelligence and Applications ss.138-143, Austria, 2007.
Year 2023, Volume: 13 Issue: 1, 13 - 22, 23.01.2023

Abstract

References

  • [1] Albayrak Makine Elektronik A.Ş., “User Manual / Application Notes of PSD-1000 system”, unpublished.
  • [2] C. Zhou, Z. Su, ve J. Zhou, “Design and Implementation of the Platform Screen Doors System for BRT”, içinde ICCTP 2010, Beijing, China, Tem. 2010, ss. 2540-2552. doi: 10.1061/41127(382)271.
  • [3]U. T. Abdurrahman, A. Jack, ve F. Schmid, “Effects of platform screen doors on the overall railway system”, program adı: 8th International Conference on Railway Engineering (ICRE 2018), London, UK, 2018. doi: 10.1049/cp.2018.0053.
  • [4] Z. Yang, X. Su, F. Ma, L. Yu, ve H. Wang, “An innovative environmental control system of subway”, Journal of Wind Engineering and Industrial Aerodynamics, c. 147, ss. 120-131, Ara. 2015, doi: 10.1016/j.jweia.2015.09.015.
  • [5] S. He, L. Jin, T. Le, C. Zhang, X. Liu, ve X. Ming, “Commuter health risk and the protective effect of three typical metro environmental control systems in Beijing, China”, Transportation Research Part D: Transport and Environment, c. 62, ss. 633-645, Tem. 2018, doi: 10.1016/j.trd.2018.04.015.
  • [6] H. Han, J.-Y. Lee, ve K.-J. Jang, “Effect of platform screen doors on the indoor air environment of an underground subway station”, Indoor and Built Environment, c. 24, sy 5, ss. 672-681, Ağu. 2015, doi: 10.1177/1420326X14528731.
  • [7] X. Li ve Y. Wang, “Simulation study on air leakage of platform screen doors in subway stations”, Sustainable Cities and Society, c. 43, ss. 350-356, Kas. 2018, doi: 10.1016/j.scs.2018.08.035.
  • [8] Z. Su ve X. Li, “Energy benchmarking analysis of subway station with platform screen door system in China”, Tunnelling and Underground Space Technology, c. 128, s. 104655, Eki. 2022, doi: 10.1016/j.tust.2022.104655.
  • [9] S.-C. Hu ve J.-H. Lee, “Influence of platform screen doors on energy consumption of the environment control system of a mass rapid transit system: case study of the Taipei MRT system”, Energy Conversion and Management, c. 45, sy 5, ss. 639-650, Mar. 2004, doi: 10.1016/S0196-8904(03)00188-2.
  • [10] O. Lindfeldt, “The impact of platform screen doors on rail capacity”, Int. J. TDI, c. 1, sy 3, ss. 601-610, Oca. 2017, doi: 10.2495/TDI-V1-N3-601-610.
  • [11] L. Qu ve W. K. Chow, “Platform siren doors on emergency evacuation in underground railway stations”, Tunnelling and Underground Space Technology, c. 30, ss. 1-9, Tem. 2012, doi: 10.1016/j.tust.2011.09.003.
  • [12] J. S. Roh, H. S. Ryou, ve S. W. Yoon, “The effect of PSD on life safety in subway station fire”, J Mech Sci Technol, c. 24, sy 4, ss. 937-942, Nis. 2010, doi: 10.1007/s12206-010-0217-7.
  • [13] L. Min, C. Zhaoyong, ve Z. Jin, “Study on PSD system control strategy for safety”, içinde 2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization, Chengdu, China, Eki. 2012, ss. 154-159. doi: 10.1109/ICSSEM.2012.6340789.
  • [14] Functional Safety of Electrical/Electronic/Programmable Electronic Safety Related Systems, IEC 61508-3, European Committee for Electrotechnical Standardization, Brussels, Belgium, 2000.
  • [15] M. S. Durmuş, S. Takai, ve M. T. Söylemez, “Fault diagnosis in fixed-block railway signaling systems: a discrete event systems approach: FAULT DIAGNOSIS IN FIXED-BLOCK RAILWAY SIGNALING SYSTEMS”, IEEJ Trans Elec Electron Eng, c. 9, sy 5, ss. 523-531, Eyl. 2014, doi: 10.1002/tee.22001.
  • [16] C. Li, S. Luo, C. Cole, ve M. Spiryagin, “An overview: modern techniques for railway vehicle on-board health monitoring systems”, Vehicle System Dynamics, c. 55, sy 7, ss. 1045-1070, Tem. 2017, doi: 10.1080/00423114.2017.1296963.
  • [17] K. Tidriri, N. Chatti, S. Verron, ve T. Tiplica, “Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges”, Annual Reviews in Control, c. 42, ss. 63-81, 2016, doi: 10.1016/j.arcontrol.2016.09.008.
  • [18] H. Dassanayake, C. Roberts, C. J. Goodman, ve A. M. Tobias, “Use of parameter estimation for the detection and diagnosis of faults on electric train door systems”, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, c. 223, sy 4, ss. 271-278, Ara. 2009, doi: 10.1243/1748006XJRR265.
  • [19] L. Shuai, J. Limin, Q. Yong, Y. Bo, ve W. Yanhui, “Research on Urban Rail Train Passenger Door System Fault DiagnosisUsing PCA and Rough Set”, TOMEJ, c. 8, sy 1, ss. 340-348, Eyl. 2014, doi: 10.2174/1874155X01408010340.
  • [20] E. Miguelanez, K. E. Brown, R. Lewis, C. Roberts, ve D. M. Lane, “Fault diagnosis of a train door system based on semantic knowledge representation”, içinde 4th IET International Conference on Railway Condition Monitoring (RCM 2008), Derby, UK, 2008, ss. 27-27. doi: 10.1049/ic:20080333.
  • [21] L. Cauffriez, S. Grondel, P. Loslever, ve C. Aubrun, “Bond Graph modeling for fault detection and isolation of a train door mechatronic system”, Control Engineering Practice, c. 49, ss. 212-224, Nis. 2016, doi: 10.1016/j.conengprac.2015.12.019.
  • [22] J. Yu, “Local and global principal component analysis for process monitoring”, Journal of Process Control, c. 22, sy 7, ss. 1358-1373, Ağu. 2012, doi: 10.1016/j.jprocont.2012.06.008.
  • [23] D. Gonzalez-Jimenez, J. del-Olmo, J. Poza, F. Garramiola, ve P. Madina, “Data-Driven Fault Diagnosis for Electric Drives: A Review”, Sensors, c. 21, sy 12, s. 4024, Haz. 2021, doi: 10.3390/s21124024.
  • [24] X. Sun, K. V. Ling, K. K. Sin, ve L. Tay, “Intelligent Fault Detection and Diagnosis of Air Leakage on Train Door”, içinde 2018 International Conference on Intelligent Rail Transportation (ICIRT), Singapore, Ara. 2018, ss. 1-4. doi: 10.1109/ICIRT.2018.8641662.
  • [25] S. Kim, N. H. Kim, ve J.-H. Choi, “Information Value-Based Fault Diagnosis of Train Door System under Multiple Operating Conditions”, Sensors, c. 20, sy 14, s. 3952, Tem. 2020, doi: 10.3390/s20143952.
  • [26] R. Chen, S. Zhu, F. Hao, B. Zhu, Z. Zhao, ve Y. Xu, “Railway Vehicle Door Fault Diagnosis Method with Bayesian Network”, içinde 2019 4th International Conference on Control and Robotics Engineering (ICCRE), Nanjing, China, Nis. 2019, ss. 70-74. doi: 10.1109/ICCRE.2019.8724211.
  • [27] Y. Sun, Y. Cao, ve L. Ma, “A Fault Diagnosis Method for Train Plug Doors via Sound Signals”, IEEE Intell. Transport. Syst. Mag., c. 13, sy 3, ss. 107-117, 2021, doi: 10.1109/MITS.2019.2926366.
  • [28] H. Chen, B. Jiang, S. X. Ding, ve B. Huang, “Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives”, IEEE Trans. Intell. Transport. Syst., c. 23, sy 3, ss. 1700-1716, Mar. 2022, doi: 10.1109/TITS.2020.3029946.
  • [29] G. W. Han vd., “Incipient anomaly detection for railway vehicle door system based on adaptive mean shift clustering”, içinde 2017 Chinese Automation Congress (CAC), Jinan, Eki. 2017, ss. 1297-1302. doi: 10.1109/CAC.2017.8242967.
  • [30] X. Heng, Q. Jiang, D. Liu, L. Xie, T. Zhan, ve N. Jin, “Fault Diagnosis of Subway Plug Door Based on KPCA and CS-LSSVM”, içinde 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway, Kas. 2020, ss. 100-105. doi: 10.1109/ICIEA48937.2020.9248245.
  • [31] S. Ham, S.-Y. Han, S. Kim, H. J. Park, K.-J. Park, ve J.-H. Choi, “A Comparative Study of Fault Diagnosis for Train Door System: Traditional versus Deep Learning Approaches”, Sensors, c. 19, sy 23, s. 5160, Kas. 2019, doi: 10.3390/s19235160.
  • [32] N. Lehrasab, H. P. B. Dassanayake, C. Roberts, S. Fararooy, ve C. J. Goodman, “Industrial fault diagnosis: Pneumatic train door case study”, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, c. 216, sy 3, ss. 175-183, May. 2002, doi: 10.1243/095440902760213602.
  • [33] P. Wen, M. Zhi, G. Zhang, ve S. Li, “Fault Prediction of Elevator Door System Based on PSO-BP Neural Network”, ENG, c. 08, sy 11, ss. 761-766, 2016, doi: 10.4236/eng.2016.811068.
  • [34] T. Ince, S. Kiranyaz, L. Eren, M. Askar, ve M. Gabbouj, “Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks”, IEEE Trans. Ind. Electron., c. 63, sy 11, ss. 7067-7075, Kas. 2016, doi: 10.1109/TIE.2016.2582729.
  • [35] Chow, M.-Y., “Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection”, Singapore: World Scientific, 1997.
  • [36] B. Bagheri, H. Ahmadi, ve R. Labbafi, “Application of data mining and feature extraction on intelligent fault diagnosis by Artificial Neural Network and k-nearest neighbor”, içinde The XIX International Conference on Electrical Machines - ICEM 2010, Rome, Italy, Eyl. 2010, ss. 1-7. doi: 10.1109/ICELMACH.2010.5607984.
  • [37] A. Boussif ve M. Ghazel, “Model-Based Monitoring of a Train Passenger Access System”, IEEE Access, c. 6, ss. 41619-41632, 2018, doi: 10.1109/ACCESS.2018.2860966.
  • [38] B. Yegnanarayana, “Artificial neural networks”, PHI Learning Pvt. Ltd., 2009.
  • [39] I. N. da Silva, R. Andrade Flauzino, S. F. dos Reis Alves, D. Hernane Spatti, ve L. H. B. Liboni, Artificial Neural Networks: A Practical Course, 1st ed. 2017. Cham: Springer International Publishing : Imprint: Springer, 2017. doi: 10.1007/978-3-319-43162-8.
  • [40] I. A. Basheer, and M. Hajmeer, “Artificial neural networks: fundamentals, computing, design, and application,” Journal of Microbiological Methods vol. 43(1), ss.3–31, 2000.
  • [41] H. Demuth, M. Beale, and M. Hagan, “Neural Network Toolbox TM 6 User’s Guide”. Network, the MathWorks, Inc.P, 2010.
  • [42] Z. Comert, A.F. Kocamaz, “A study of artificial neural network training algorithms for classification of cardiotocography signals,” Bitlis Eren University Journal Of Science And Technology vol. 7(2), ss 93–103, 2017.
  • [43] Y. Liu, J. Starzyk, Z. Zhu, “Optimizing number of hidden neurons in neural networks.. IASTED International Conference on Artificial Intelligence and Applications ss.138-143, Austria, 2007.
There are 43 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Akademik ve/veya teknolojik bilimsel makale
Authors

İsa Koç

Ömer Mermer

Necim Kırımça

Mehmet Karaköse 0000-0002-3276-3788

Publication Date January 23, 2023
Submission Date October 13, 2022
Published in Issue Year 2023 Volume: 13 Issue: 1

Cite

APA Koç, İ., Mermer, Ö., Kırımça, N., Karaköse, M. (2023). Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı. EMO Bilimsel Dergi, 13(1), 13-22.
AMA Koç İ, Mermer Ö, Kırımça N, Karaköse M. Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı. EMO Bilimsel Dergi. January 2023;13(1):13-22.
Chicago Koç, İsa, Ömer Mermer, Necim Kırımça, and Mehmet Karaköse. “Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı”. EMO Bilimsel Dergi 13, no. 1 (January 2023): 13-22.
EndNote Koç İ, Mermer Ö, Kırımça N, Karaköse M (January 1, 2023) Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı. EMO Bilimsel Dergi 13 1 13–22.
IEEE İ. Koç, Ö. Mermer, N. Kırımça, and M. Karaköse, “Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı”, EMO Bilimsel Dergi, vol. 13, no. 1, pp. 13–22, 2023.
ISNAD Koç, İsa et al. “Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı”. EMO Bilimsel Dergi 13/1 (January 2023), 13-22.
JAMA Koç İ, Mermer Ö, Kırımça N, Karaköse M. Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı. EMO Bilimsel Dergi. 2023;13:13–22.
MLA Koç, İsa et al. “Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı”. EMO Bilimsel Dergi, vol. 13, no. 1, 2023, pp. 13-22.
Vancouver Koç İ, Mermer Ö, Kırımça N, Karaköse M. Raylı Sistemlerde Peron Ayırıcı Kapı Sistemi İçin Yapay Sinir Ağı Tabanlı Hata Teşhis Yaklaşımı. EMO Bilimsel Dergi. 2023;13(1):13-22.

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