رویکرد بهبود یافته برنامهریزی نگهداری و تعمیرات خطوط ریلی بر اساس لرزهنگاری در داخل کابین قطار با دستگاههای تلفن همراه | ||
مدیریت مهندسی و رایانش نرم | ||
دوره 11، شماره 1 - شماره پیاپی 20، مرداد 1404، صفحه 81-48 اصل مقاله (1.37 M) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22091/jemsc.2025.11229.1204 | ||
نویسندگان | ||
بابک جوادی* 1؛ سعید کیااحمدی2؛ امیرحسین عباسی2؛ محمدرضا ابدالی2 | ||
1گروه مهندسی صنایع، دانشکده مهندسی، دانشکدگان فارابی، دانشگاه تهران | ||
2گروه مهندسی صنایع، دانشکده مهندسی، دانشکدگان فارابی، دانشگاه تهران، ایران | ||
چکیده | ||
افزایش سرعت قطارها، حجم تردد ریلی به طور قابل توجهی افزایش یافته است. این افزایش هم در تعداد مسافران و هم در میزان بار منتقل شده مشهود است. عوامل مختلفی مانند سرعت حرکت قطارها، وزن بار و تردد ریلی بر کیفیت مسیر ریلی تأثیرگذار هستند. توسعه شبکه ریلی باعث افزایش نرخ تغییرات هندسی مسیر شده و در نتیجه، کیفیت مسیر به مرور زمان کاهش مییابد. تغییرات شدید هندسی مسیر میتواند خطرات جدی مانند خروج قطار از ریل را به همراه داشته باشد. به همین دلیل، شرکتهای ریلی هزینههای هنگفتی را صرف بازرسی دورهای مسیر میکنند.این پژوهش روشی نوین و کمهزینه برای پایش مستمر وضعیت مسیر ریلی ارائه میدهد. در این روش، از قابلیتهای تلفنهای همراه مسافران برای ثبت لرزشهای واگن در جهتهای عمودی و افقی استفاده میشود. با تحلیل این دادهها، میتوان وضعیت هندسی مسیر را پیشبینی کرده و نواحی با مشکل را شناسایی نمود. این روش نه تنها هزینههای بازرسی را به شدت کاهش میدهد، بلکه امکان شناسایی زودهنگام مشکلات و جلوگیری از بروز حوادث را فراهم میکند. | ||
کلیدواژهها | ||
"برنامهریزی نگهداری و تعمیرات"؛ "خطوط حمل و نقل ریلی"؛ "پارامترهای هندسی"؛ "لرزهنگاری"؛ "پایش وضعیت" | ||
عنوان مقاله [English] | ||
Improved maintenance planning of railway tracks based on onboard vibration measurements using smartphones | ||
نویسندگان [English] | ||
Babak Javadi1؛ Saeed Kiaahmadi2؛ Amirhosein Abasi2؛ Mohammadreza Abdali2 | ||
1Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran | ||
2. Department of Industrial Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran | ||
چکیده [English] | ||
With the increase in train speed, railway traffic has grown significantly in terms of both passenger numbers and cargo weight. Speed, weight, and rail traffic collectively impact rail track quality. The expanding rail network has accelerated the rate of track geometry changes, leading to deteriorating track conditions. Severe track geometry deviations pose significant risks, including train derailment. Consequently, railway companies invest substantial resources in regular track inspections to mitigate these risks. This article proposes a cost-effective and reliable method for continuous track geometry monitoring during every journey. By utilizing the widespread availability of smartphones, we aim to record wagon vibrations to predict rail conditions in both vertical and horizontal dimensions. This approach can drastically reduce maintenance costs by efficiently identifying areas requiring urgent attention. | ||
کلیدواژهها [English] | ||
Horizontal track geometry, vertical track geometry, smartphone, vibration, track acceleration | ||
مراجع | ||
Ackroyd, P., Angelo, S., & Stevens, J. (2002). Remote ride quality monitoring of Acela train set performance. ASME/IEEE Joint Railroad Conference, 171–178. https://doi.org/10.1109/RRCON.2002.1000109 Andian Technologies, www.andian.com. 2014.
Antognoli, M., Marinacci, C., Ricci, S., & Rizzetto, L. (2020). Requirement specifications for track measuring and monitoring systems. Ingegneria Ferroviaria, 2020, 841–864. Bababeik, M., Behnia, K., & Khademi, N. (2024). Increasing the Resilience of Rail Network through the Optimal Location of Relief Trains. Journal of Transportation Research, 21(2), 217–232. https://www.trijournal.ir/article_96254.html Bahrekazemi, M. (2004). Train-Induced Ground Vibration and Its Prediction. Bokhman, E. D., Boronachin, A. M., Filatov, Yu. V, Larionov, D. Yu., Podgornaya, L. N., Shalymov, R. V, & Zuzev, G. N. (2014). Optical-inertial system for railway track diagnostics. 2014 DGON Inertial Sensors and Systems (ISS), 1–17. https://doi.org/10.1109/InertialSensors.2014.7049477 Heirich, O., Lehner, A., Robertson, P., & Strang, T. (2011). Measurement and analysis of train motion and railway track characteristics with inertial sensors. 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 1995–2000. https://doi.org/10.1109/ITSC.2011.6082908 Islamic Republic of Iran Railways website.
Kaito, K., Ishii, H., Fujino, Y., & Mizuno, Y. (2006). The Study of Train Intelligent Monitoring System Using Acceleration of Ordinary Train. https://api.semanticscholar.org/CorpusID:54676958 Kratochwille, R. (2019, آگوست). ICE-S Vehicle reaction measurement and track geometry measurement on the same measuring train Results of the comparison of the two different track inspection methods. Lee, J. S., Choi, S., Kim, S. S., Kim, Y. G., Kim, S. W., & Park, C. (2011). Track condition monitoring by in-service trains: A comparison between axle-box and bogie accelerometers. IET Conference Publications, 2011(581 CP). https://doi.org/10.1049/cp.2011.0586 Yavari, M., & Akbari, A. H. (2023). Service level and profit maximisation in order acceptance and scheduling problem with weighted tardiness. International Journal of Industrial and Systems Engineering, 43(3), 331-362. https://doi.org/10.1504/IJISE.2023.129138 Akbari, A. H., & Jafari, M. (2025). Development of a Deep Reinforcement Learning Algorithm in a Dynamic Cellular Manufacturing System Considering Order Rejection, Case Study: Stone Paper Factory. Engineering Management and Soft Computing, 10(2), 204-222. doi: 10.22091/jemsc.2025.11853.1230 Jabbari, M., Rezaeenour, J., & Akbari, A. H. (2023). A Feature Selection Method Based on Information Theory and Genetic Algorithm. Sciences and Techniques of Information Management, 9(3), 32-7. doi: 10.22091/stim.2023.8708.1877 McAnaw, H. E. (2003). The system that measures the system. NDT & E International, 36(3), 169–179. https://doi.org/https://doi.org/10.1016/S0963-8695(02)00055-5 Molodova, M., Li, Z., Nunez, A., & Dollevoet, R. (2014). Automatic detection of squats in railway infrastructure. IEEE Transactions on Intelligent Transportation Systems, 15(5), 1980–1990. https://doi.org/10.1109/TITS.2014.2307955 MORI, H., TSUNASHIMA, H., KOJIMA, T., MATSUMOTO, A., & MIZUMA, T. (2010). Condition Monitoring of Railway Track Using In-service Vehicle. Journal of Mechanical Systems for Transportation and Logistics, 3(1), 154–165. https://doi.org/10.1299/jmtl.3.154 Rossi, F., & Nicolini, A. (2003). A simple model to predict train-induced vibration: theoretical formulation and experimental validation. Environmental Impact Assessment Review, 23(3), 305–322. https://doi.org/https://doi.org/10.1016/S0195-9255(03)00005-2 Weston, P. F., Ling, C. S., Roberts, C., Goodman, C. J., Li, P., & Goodall, R. M. (2007). Monitoring vertical track irregularity from in-service railway vehicles. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 221(1), 75–88. https://doi.org/10.1243/0954409JRRT65 Tsunashima, H., Honda, R., & Matsumoto, A. (2023). Track Condition Monitoring Based on In-Service Train Vibration Data Using Smartphones. In Railway Infrastructure Maintenance (pp. 1–20). IntechOpen. https://doi.org/10.5772/intechopen.108123
Mohammadzadeh, S., Heydari, H., Karimi, M., & Mosleh, A. (2024). Correlation Analysis of Railway Track Alignment and Ballast Stiffness: Comparing Frequency-Based and Machine Learning Algorithms. Journal of Rail Transport Planning & Management, 11(1), 23–45. https://doi.org/10.1016/j.jrtpm.2023.100234 Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394. https://doi.org/10.1016/j.eswa.2023.122394 Yavari, M., Marvi, M., & Akbari, A. H. (2020). Semi-permutation-based genetic algorithm for order acceptance and scheduling in two-stage assembly problem. Neural Computing and Applications, 32, 2989-3003. https://doi.org/10.1007/s00521-019-04027-w Tanhaeean, M., Tavakkoli-Moghaddam, R., & Akbari, A. H. (2022). Boxing match algorithm: A new meta-heuristic algorithm. Soft Computing, 26(24), 13277-13299. https://doi.org/10.1007/s00500-022-07518-6 Meng, Q., Lu, P., & Zhu, S. (2023). A Smartphone-Enabled IoT System for Vibration and Noise Monitoring of Rail Transit. IEEE Internet of Things Journal, 9(7), 5678–5687. https://doi.org/10.1109/JIOT.2022.3233051 | ||
آمار تعداد مشاهده مقاله: 59 تعداد دریافت فایل اصل مقاله: 29 |