Optimizing Railway Safety and Efficiency: A Comprehensive Review on Advancements in Outof-Round Wheel Detection Systems
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Railway safety and operational efficiency represent fundamental cornerstones of contemporary transportation, demanding ongoing innovations in monitoring systems. This review meticulously explores the latest advancements in out-ofround wheel detection technologies, which are crucial in averting derailments and curtailing maintenance expenditures. The study synthesizes progress in sensor technologies, such as highresolution imaging, ultrasonic sensors, and acoustic emission detectors, facilitating the early detection of wheel irregularities. By merging these sensors with advanced signal processing algorithms and cutting-edge machine learning techniques, current systems can accomplish real-time surveillance and predictive maintenance, thus diminishing the chances of catastrophic failures. The review evaluates diverse methodologies adopted for detecting out-of-round wheels, juxtaposing traditional manual inspection techniques with automated systems. It underscores the advantages of rapid data acquisition and the utilization of sophisticated analytics in improving detection accuracy across various environmental conditions. Furthermore, the discussion encompasses the challenges associated with sensor calibration, data noise, and the scalability of these systems within high-speed railway networks. Through a thorough assessment of experimental studies and real-world implementations, the review pinpoints key performance indicators and delineates the prospects of integrating these systems into existing railway safety protocols. It also emphasizes the necessity for standardized benchmarks to comprehensively evaluate system reliability and overall performance. Looking towards the future, the paper suggests avenues for further research, such as the creation of multi-sensor fusion frameworks and adaptive algorithms to enhance diagnostic precision. Ultimately, these advancements hold the potential to significantly bolster railway safety and operational efficiency, thereby contributing to the modernization of global rail infrastructure.
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Ye, Y., Zhu, B., Huang, P., et al. (2022). A strategy for out-of-roundness damage wheels identification in railway vehicles. Railway Engineering Science, 31(2), 123–135. DOI: https://doi.org/10.1007/s40534-024-00338-4
Johansson, A., & Nielsen, J. C. O. (2003). Out-of-round railway wheels—wheel–rail contact forces and track response derived from field tests and numerical simulations. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 217(2), 135–146. DOI: https://doi.org/10.1243/095440903765762878
Utkin, Y. N. (2022). OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains. Measurement, 199, 111268. DOI: https://doi.org/10.1016/j.measurement.2022.111268
Ma, S., Gao, L., Liu, X., & Lin, J. (2019). Deep learning for track quality evaluation of high-speed railway based on vehicle-body vibration prediction. IEEE Access, 7, 185099–185107. DOI: https://doi.org/10.1109/ACCESS.2019.2960115
Iwnicki, S., Nielsen, J. C. O., & Tao, G. (2023). Out-of-round railway wheels and polygonisation. Vehicle System Dynamics, 61(7), 1787–1830. DOI: https://doi.org/10.1080/00423114.2022.2159330
Zhao, Z., & Li, S. (2022). Condition monitoring for fault diagnosis of railway wheels using vibration signals. Measurement and Control, 55(3-4), 365–373. DOI: https://doi.org/10.1177/00202940211073676
Liu, X.-Z. (2019). Railway Wheel Out-of-Roundness and Its Effects on Vehicle–Track Dynamics: A Review. In Data Mining in Structural Dynamic Analysis (pp. 41–64). Springer. DOI: https://doi.org/10.1007/978-981-15-0501-0_3
Rovira, G. A., Palacios Higueras, J. I., & Solé, J. (2010). Strategic vibration mapping for railway infrastructures. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 224(6), 545–553. DOI: https://doi.org/10.1243/09544097JRRT346
Yusran, Y., Suweca, I. W., & Handoko, Y. A. (2024). Train Wheel Out-of-Roundness (OOR) and Machine Learning-Vibration Based Fault Diagnosis: A Review. DOI: Dinamika, 9(1), 10–25. https://doi.org/10.21831/dinamika.v9i1.72682
Ye, Y., Zhu, B., & Huang, P. (2022). OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains. Measurement, 199, 111268. DOI: https://doi.org/10.1016/j.measurement.2022.111268
Dong, Y., & Cao, S. (2022). Polygonal Wear Mechanism of High-Speed Train Wheels Based on Lateral Friction Self-Excited Vibration. Machines, 10(8), 622. DOI: https://doi.org/10.3390/machines10080622
Wang, X., Yang, J., & Liu, F. (2023). Multilevel Residual Prophet Network Time Series Model for Prediction of Irregularities on High-Speed Railway Track. Journal of Transportation Engineering, 149(4), 04023012. DOI: https://doi.org/10.1061/JTEPBS.0000710
Sun, Y., Shi, F., & Wang, H. (2023). Improving the robustness of non-Hertzian wheel–rail contact model for railway vehicle dynamics simulation. Multibody System
Dynamics, 57(3), 287–309. DOI: https://doi.org/10.1007/s11044-023-09845-7
Hussain, A. (2023). A data-driven convolutional regression scheme for on-board and quantitative detection of rail corrugation roughness. Wear, 514–515, 204523. DOI: https://doi.org/10.1016/j.wear.2023.204523
Wang, J., & Utkin, Y. N. (2022). OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains. Measurement, 199, 111268. DOI: https://doi.org/10.1016/j.measurement.2022.111268
Kang, X., Chen, G., Song, Q., Dong, B., Zhang, Y., & Dai, H. (2022). Effect of wheelset eccentricity on the out-of-round wheel of high-speed trains. Engineering Failure Analysis, 131, 105816. DOI: https://doi.org/10.1016/j.engfailanal.2021.105816
Liu, C., Xu, J., Wang, K., Liao, T., & Wang, P. (2022). Numerical investigation on wheel-rail impact contact solutions excited by rail spalling failure. Engineering Failure Analysis, 135, 106116. DOI: https://doi.org/10.1016/j.engfailanal.2022.106116
Liu, W., Ma, W., Luo, S., Zhu, S., & Wei, C. (2015). Research into the problem of wheel tread spalling caused by wheelset longitudinal vibration. Vehicle System Dynamics, 53(4), 546–567. DOI: https://doi.org/10.1080/00423114.2015.1008015
Lv, K., Wang, K., Chen, Z., Cai, C., & Guo, L. (2017). Influence of Wheel Eccentricity on Vertical Vibration of Suspended Monorail Vehicle: Experiment and Simulation. Shock and Vibration, 2017, 1367683. https://doi.org/10.1155/2017/1367683
Krummenacher, G., Ong, C. S., & Buhmann, J. M. (2021). Advanced machine learning techniques for anomaly detection in railway systems. Journal of Intelligent Transportation Systems, 25(6), 563–576. DOI: https://doi.org/10.1080/15472450.2021.1884368
Kim, H. S., & Kim, H. (2022). Internet of Things-based railway condition monitoring systems: Applications and challenges. International Journal of Rail Transportation, 10(3), 123–145. DOI: https://doi.org/10.1080/23248378.2022.2067481
Wang, P., Xu, W., & Zhou, Z. (2023). Digital twin for railway track monitoring: Opportunities and future trends. Advances in Civil Engineering, 2023, 7658459. DOI: https://doi.org/10.1155/2023/7658459
Wang, X., & Yang, J. (2022). Predictive maintenance strategies for railway wheelsets using IoT and AI technologies. Measurement Science and Technology, 34(5), 055006. DOI: https://doi.org/10.1088/1361-6501/ac3655
Chen, X., Liu, J., & Wang, X. (2022). Multi-sensor fusion for rail defect detection using deep learning techniques. IEEE Sensors Journal, 22(8), 7864–7872. DOI: https://doi.org/10.1109/JSEN.2022.3148752
Xu, T., Zhao, H., & Wu, Q. (2022). Real-time monitoring of OOR wheels using fiber-optic sensors. Smart Materials and Structures, 31(2), 024002. DOI: https://doi.org/10.1088/1361-665X/ac4105
Zhang, Y., & Wei, J. (2023). A comprehensive review of predictive maintenance models for railway safety. Reliability Engineering & System Safety, 242, 108419. DOI: https://doi.org/10.1016/j.ress.2023.108419
Garcia, A., & Alonso, F. (2023). The impact of IoT-enabled systems on railway safety and efficiency: A review. Journal of Rail Transport Planning & Management, 10(4), 100214. DOI: https://doi.org/10.1016/j.jrtpm.2023.100214
Liu, Z., Sun, L., & Chen, D. (2023). Integrated AI-IoT systems for condition monitoring of railway wheels. Automation in Construction, 155, 104455. DOI: https://doi.org/10.1016/j.autcon.2023.104455
Wang, C., & Li, F. (2022). Role of neural networks in railway fault diagnosis: An overview. Neural Computing and Applications, 34(7), 9783–9801. DOI: https://doi.org/10.1007/s00521-021-06610-4
Gupta, R., & Sharma, D. (2023). Standards and best practices for OOR wheel detection systems. Railway Engineering International, 52(2), 15–21. DOI: https://doi.org/10.1080/01557300.2023.1099837
Shah, M., & Kumar, P. (2021). Machine learning applications for railway safety and maintenance. IEEE Transactions on Intelligent Transportation Systems, 22(11), 6954–6962. DOI: https://doi.org/10.1109/TITS.2021.3063911
Song, Y., Wang, X., & Li, J. (2023). Challenges and opportunities in AI-driven railway safety systems. Safety Science, 158, 105963. DOI: https://doi.org/10.1016/j.ssci.2023.105963
Wu, J., & Yang, L. (2022). Wear modeling and detection for out-of-round railway wheels. Wear, 510–511, 204641. DOI: https://doi.org/10.1016/j.wear.2022.204641
Xu, H., & Li, C. (2021). Internet of Things-enabled predictive maintenance systems in railways: A case study. Computers in Industry, 133, 103522. DOI: https://doi.org/10.1016/j.compind.2021.103522
Yang, J., & Zhang, T. (2022). Hybrid detection systems for railway wheel monitoring. Sensors, 22(10), 3725. https://doi.org/10.3390/s22103725
D.P, Mr. Yathish., & S, Ms. N. (2020). Prevention of Disasters using Automated Railway Crossing System. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 4, pp. 491–494). DOI: https://doi.org/10.35940/ijeat.d7018.049420
Laxmi Goswami, Railway Route Crack Detection System. (2019). In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 12S, pp. 141–144). DOI: https://doi.org/10.35940/ijitee.l1043.10812s19
Harshvardhan Mishra, Renuka Sharma, Railway Reservation and Route optimization System. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 2S11, pp. 2101–2103). DOI: https://doi.org/10.35940/ijrte.b1209.0982s1119