Yasser Yusran, Institut Teknologi Bandung, Indonesia
I Wayan Suweca, Institut Teknologi Bandung, Indonesia
Yunendar Aryo Handoko, Institut Teknologi Bandung, Indonesia


This article aims to give a complete review of previous and current research on numerous types of out-of-roundness (OOR) failures in train wheels, as well as diagnostic approaches based on machine learning and vibration data. The study provides a comprehensive overview of the current state of research by categorizing reviews into three primary domains: (1) types of OOR failures in train wheels, (2) fault diagnosis methodologies, and (3) the use of machine learning and vibration data to diagnose train wheel OOR failures. Initially, the study investigates the characteristics, causes, and consequences of railway wheel OOR failures, including their impact on vibrations. It then dives further into diagnostic methods, comparing the effectiveness of statistical methods to machine learning-based methods for diagnosing failures. Furthermore, the study addresses current advances in machine learning and vibration-based diagnostic methods to diagnose train wheel OOR failures, providing information on their applications and results. This article highlights that by utilizing machine learning methods with vibration data offers a promising way for accurately diagnosing OOR faults in train wheels and predicting their potential failure and remaining useful life, resulting to enhanced maintenance efficiency and less downtime.


fault diagnosis; machine learning; train wheel out-of-roundness; vibration

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Abid, A., Khan, M. T., & Iqbal, J. (2021). A review on fault detection and diagnosis techniques: basics and beyond. Artificial Intelligence Review, 54(5), 3639–3664.

Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges. Applied Sciences (Switzerland), 12(16).

Bracciali, A., & Cascini, G. (1997). Detection of corrugation and wheelflats of railway wheels using energy and cepstrum analysis of rail acceleration. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 211(2), 109–116.

Braghin, F., Bruni, S., & Lewis, R. (2009). 6 - Railway wheel wear. In R. Lewis & U. Olofsson (Eds.), Wheel–Rail Interface Handbook (pp. 172–210). Woodhead Publishing.

Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. da P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers and Industrial Engineering, 137(September), 106024.

Chi, Z., Lin, J., Chen, R., & Huang, S. (2020). Data-driven approach to study the polygonization of high-speed railway train wheel-sets using field data of China’s HSR train. Measurement: Journal of the International Measurement Confederation, 149, 107022.

Chiello, O., Le Bellec, A., Pallas, M. A., Munoz, P., & Janillon, V. (2019). Characterisation of wheel/rail roughness and track decay rates on a tram network. INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control Engineering.

Chong, S. Y., Lee, J. R., & Shin, H. J. (2010). A review of health and operation monitoring technologies for trains. Smart Structures and Systems, 6(9), 1079–1105.

Çinar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability (Switzerland), 12(19).

Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 123, 103298.

Escobet, T., Bregon, A., Pulido, B., & Puig, V. (2019). Fault Diagnosis of Dynamic Systems: Quantitative and Qualitative Approaches. In Fault Diagnosis of Dynamic Systems: Quantitative and Qualitative Approaches.

Fadzail, N. F., Mat Zali, S., Mid, E. C., & Jailani, R. (2022). Application of Automated Machine Learning (AutoML) Method in Wind Turbine Fault Detection. Journal of Physics: Conference Series, 2312(1).

Fröhling, R., Spangenberg, U., & Reitmann, E. (2019). Root cause analysis of locomotive wheel tread polygonisation. Wear, 432–433(April).

Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., Mori, M., & Helu, M. (2015). Cloud-enabled prognosis for manufacturing. CIRP Annals - Manufacturing Technology, 64(2), 749–772.

Gonzalez-Jimenez, D., Del-Olmo, J., Poza, J., Garramiola, F., & Madina, P. (2021). Data-driven fault diagnosis for electric drives: A review. Sensors, 21(12).

Guedes, A., Silva, R., Ribeiro, D., Vale, C., Mosleh, A., Montenegro, P., & Meixedo, A. (2023). Detection of Wheel Polygonization Based on Wayside Monitoring and Artificial Intelligence. Sensors, 23(4).

Jelila, Y. D., & Pamuła, W. (2022). Detection of Tram Wheel Faults Using MEMS-Based Sensors. Sensors, 22(17).

Jing, L., Liu, Z., & Liu, K. (2022). A mathematically-based study of the random wheel-rail contact irregularity by wheel out-of-roundness. Vehicle System Dynamics, 60(1), 335–370.

Jing, L., Wang, K., & Zhai, W. (2021). Impact vibration behavior of railway vehicles: a state-of-the-art overview. Acta Mechanica Sinica/Lixue Xuebao, 37(8), 1193–1221.

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(August 2021), 105816.

Komite Nasional Keselamatan Transportasi Republik Indonesia. (2018). Laporan Akhir KNKT. In Laporan Investigasi Kecelakaan Perkeretaapian.

Kou, L., Qin, Y., & Zhao, X. (2018). An Integrated Model of kNN and GBDT for Fault Diagnosis of Wheel on Railway Vehicle. Proceedings - 2018 Prognostics and System Health Management Conference, PHM-Chongqing 2018, 432–436.

Krummenacher, G., Ong, C. S., Koller, S., Kobayashi, S., & Buhmann, J. M. (2018). Wheel Defect Detection with Machine Learning. IEEE Transactions on Intelligent Transportation Systems, 19(4), 1176–1187.

Lee, W. J., Wu, H., Yun, H., Kim, H., Jun, M. B. G., & Sutherland, J. W. (2019). Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP, 80, 506–511.

Li, C. (2022). Wheel Polygon Detection Based on Vibration-Impact Analyses of Bogie Components. ICRT 2021 - Proceedings of the 2nd International Conference on Rail Transportation, 267–275.

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(February), 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.

Liu, X.-Z. (2019). Railway Wheel Out-of-Roundness and Its Effects on Vehicle–Track Dynamics: A Review. Data Mining in Structural Dynamic Analysis, 41–64.

Liu, X. Z. (2019). Railway Wheel Out-of-Roundness and Its Effects on Vehicle-Track Dynamics: A Review. Data Mining in Structural Dynamic Analysis: A Signal Processing Perspective, 41–64.

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.

Moyar, G. J., & Stone, D. H. (1991). An analysis of the thermal contributions to railway wheel shelling. Wear, 144(1–2), 117–138.

Nacchia, M., Fruggiero, F., Lambiase, A., & Bruton, K. (2021). A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector. Applied Sciences (Switzerland), 11(6), 1–34.

Ni, Y. Q., & Zhang, Q. H. (2021). A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring. Structural Health Monitoring, 20(4), 1536–1550.

Nielsen, J. (2009). 8 - Out-of-round railway wheels. In R. Lewis & U. Olofsson (Eds.), Wheel–Rail Interface Handbook (pp. 245–279). Woodhead Publishing.

Nielsen, J. C. O., & Johansson, A. (2000). Out-of-round railway wheels-a literature survey. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 214(2), 79–91.

Nielsen, J. C. O., Lombaert, G., & François, S. (2015). A hybrid model for prediction of ground-borne vibration due to discrete wheel/rail irregularities. Journal of Sound and Vibration, 345, 103–120.

Osman, H., & Yacout, S. (2022). Condition-based monitoring of the rail wheel using logical analysis of data and ant colony optimization. Journal of Quality in Maintenance Engineering, 29(2), 377–400.

P. Nunes, J. Santos, E. R. (2023). Challenges in predictive maintenance – A review. CIRP Journal of Manufacturing Science and Technology, 40, 53–67.

Papaelias, M., Amini, A., Huang, Z., Vallely, P., Dias, D. C., & Kerkyras, S. (2016). Online condition monitoring of rolling stock wheels and axle bearings. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 230(3), 709–723.

Pau, M. (2005). Ultrasonic waves for effective assessment of wheel-rail contact anomalies. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 219(2), 79–90.

Peng, B. (2020). Mechanisms of railway wheel polygonization. University of Huddersfield.

Ran, Y., Zhou, X., Lin, P., Wen, Y., & Deng, R. (2019). A Survey of Predictive Maintenance: Systems, Purposes and Approaches. XX(Xx), 1–36.

Shaikh, M. Z., Ahmed, Z., Chowdhry, B. S., Baro, E. N., Hussain, T., Uqaili, M. A., Mehran, S., Kumar, D., & Shah, A. A. (2023). State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review. IEEE Access, 11(December 2022), 13257–13279.

Sohail, A., Cheema, M. A., Ali, M. E., Toosi, A. N., & Rakha, H. A. (2023). Data-driven approaches for road safety: A comprehensive systematic literature review. Safety Science, 158(August 2022).

Srivastava, J. P., Sarkar, P. K., & Ranjan, V. (2016). Effects of thermal load on wheel–rail contacts: A review. Journal of Thermal Stresses, 39(11), 1389–1418.

Sun, Q., Chen, C., Kemp, A. H., & Brooks, P. (2021). An on-board detection framework for polygon wear of railway wheel based on vibration acceleration of axle-box. Mechanical Systems and Signal Processing, 153, 107540.

Thakkar, N. A., Steel, J. A., Reuben, R. L., Knabe, G., Dixon, D., & Shanks, R. L. (2006). Monitoring of rail-wheel interaction using Acoustic Emission (AE). Advanced Materials Research, 13–14, 161–168.

Tiboni, M., Remino, C., Bussola, R., & Amici, C. (2022). A Review on Vibration-Based Condition Monitoring of Rotating Machinery. Applied Sciences (Switzerland), 12(3).

Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis. Part II: Qualitative models and search strategies. Computers & Chemical Engineering, 27(3), 313–326.

Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis. Part I: Quantitative model-based methods. Computers & Chemical Engineering, 27(3), 327–346.

Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis. Part III: Process history based methods. Computers and Chemical Engineering, 27, 327–346.

Verkhoglyad, A. G., Kuropyatnik, I. N., Bazovkin, V. M., & Kuryshev, G. L. (2008). Infrared diagnostics of cracks in railway carriage wheels. Russian Journal of Nondestructive Testing, 44(10), 664–668.

Wan, T. H., Tsang, C. W., Hui, K., & Chung, E. (2023). Anomaly detection of train wheels utilizing short-time Fourier transform and unsupervised learning algorithms. Engineering Applications of Artificial Intelligence, 122(February).

Wang, W., Guo, J., & Liu, Q. (2013). Experimental study on wear and spalling behaviors of railway wheel. Chinese Journal of Mechanical Engineering (English Edition), 26(6), 1243–1249.

Wang, W., He, Q., Cui, Y., & Li, Z. (2018). Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: Multitask Learning Approach. Journal of Transportation Engineering, Part A: Systems, 144(6).

Xiaoyi, H., Haoran, Z., Zhikun, S., Yinqing, H., & Lan, L. (2018). Study on influence of high-order wheel polygon wear on dynamic performance of high-speed EMU vehicle. 11th International Conference on Contact Mechanics and Wear of Rail/Wheel Systems, Delft, The Netherlands.

Xie, B., Chen, S., Xu, M., Yang, Y., & Wang, K. (2022). Polygonal Wear Identification of Wheels Based on Optimized Multiple Kernel Extreme Learning Machine. Lixue Xuebao/Chinese Journal of Theoretical and Applied Mechanics, 54(7), 1797–1806.

Xiong, L., Lv, L., Jiang, Y., Hua, C., & Dong, D. (2022). Multi-fault Classification of Train Wheelset System. Journal of Physics: Conference Series, 2184(1).

Xu, G., Hou, D., Qi, H., & Bo, L. (2021). High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life. Mechanical Systems and Signal Processing, 146.

Xu, M., & Yao, H. (2023). Fault diagnosis method of wheelset based on EEMD-MPE and support vector machine optimized by quantum-behaved particle swarm algorithm. Measurement: Journal of the International Measurement Confederation, 216(March).

Yan, B., Ma, X., Huang, G., & Zhao, Y. (2021). Two-stage physics-based Wiener process models for online RUL prediction in field vibration data. Mechanical Systems and Signal Processing, 152, 107378.

Ye, Y., Huang, C., Zeng, J., Zhou, Y., & Li, F. (2023). Shock detection of rotating machinery based on activated time-domain images and deep learning: An application to railway wheel flat detection. Mechanical Systems and Signal Processing, 186(May 2022).

Ye, Y., Shi, D., Krause, P., Tian, Q., & Hecht, M. (2020). Wheel flat can cause or exacerbate wheel polygonization. Vehicle System Dynamics, 58(10), 1575–1604.

Ye, Y., Zhu, B., Huang, P., & Peng, B. (2022). OORNet: A deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains. Measurement: Journal of the International Measurement Confederation, 199(February), 111268.

Zhang, K., Li, Y., Scarf, P., & Ball, A. (2011). Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks. Neurocomputing, 74(17), 2941–2952.

Zurek, Z. H. (2006). Magnetic monitoring of the fatigue process of the rim material of railway wheel sets. NDT and E International, 39(8), 675–679.



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