Recent advances in wayside condition monitoring for railways: a comprehensive review

Abstract

The demand for faster, longer, and safer railway networks has intensified the need for robust condition monitoring systems, particularly those capable of detecting rolling stock defects such as wheel flats, bearing faults, hunting movement, overloading, and unbalanced loads. Effective monitoring and timely maintenance are essential to mitigate these issues, enhancing both reliability and safety in railway operations. This paper presents a comprehensive review of wayside condition monitoring (WCM) systems. The study begins with a structured overview of WCM architectures, followed by a bibliometric analysis that highlights recent research trends in machine learning applications for railway condition monitoring. A detailed classification of rolling stock defects is provided to establish the foundation for condition assessment. The review then presents an extensive survey of commercial wayside monitoring systems currently in use, including hot axle box detectors, wheel impact load detectors, acoustic detection systems, and weigh-in-motion technologies. The various sensor types integrated into these systems are also described in detail. To bridge the gap between raw data collection and actionable insights, the paper includes a dedicated section on knowledge extraction from WCM systems. This section outlines key approaches for fault detection, diagnosis, and classification using advanced data processing methods, including signal processing and machine learning techniques. Finally, the paper identifies critical challenges such as data quality, real-time processing constraints, infrastructure limitations, and the need for generalizable models. It also discusses research gaps and suggests future directions, including the integration of edge computing, digital twin technology, and self-diagnostic capabilities to support the development of next-generation intelligent WCM systems.

Keywords

Artificial intelligence / Fault diagnosis / Railways / Wayside condition monitoring / Sensors

References

[1] Fetting C (2020) The European green deal. In: ESDN Report. ESDN, Vienna

[2] Alemi A, Corman F, Lodewijks G. Condition monitoring approaches for the detection of railway wheel defects. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2017, 231(8): 961-981

[3] Ahmed Murtaza A, Saher A, Hamza Zafar Met al.. Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: a systematic review, challenges and case study. Results Eng, 2024, 24 102935

[4] Krishnamurthi R, Kumar A, Gopinathan Det al.. An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors, 2020, 20(21 6076

[5] Oladimeji D, Gupta K, Kose NAet al.. Smart transportation: an overview of technologies and applications. Sensors, 2023, 238 3880

[6] Mosleh A, Montenegro PA, Costa PA et al (2022) Approaches for weigh-in-motion and wheel defect detection of railway vehicles. Rail Infrastructure Resilience. Elsevier, pp 183–207

[7] Shaikh MZ, Ahmed Z, Chowdhry BSet al.. State-of-the-art wayside condition monitoring systems for railway wheels: a comprehensive review. IEEE Access, 2023, 11: 13257-13279

[8] Guedes A, Silva R, Ribeiro Det al.. Clustering-based classification of polygonal wheels in a railway freight vehicle using a wayside system. Appl Sci, 2024, 149 3650

[9] Magalhães J, Jorge T, Silva Ret al.. A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders. Railw Eng Sci, 2024, 324): 421-443

[10] Mosleh A, Meixedo A, Ribeiro Det al.et al.. Ribeiro D, Montenegro PA, Andersson Aet al.et al.. Condition monitoring of rolling stock supported by artificial intelligence technique. Digital Railway Infrastructure, 2024, Cham, Springer117133

[11] Dong M, Chen S, Xie Bet al.. A quantitative detection method for wheel polygonization of heavy-haul locomotives based on a hybrid deep learning model. Measurement, 2024, 227 114206

[12] Shaikh K, Hussain I, Chowdhry BS. Wheel defect detection using a hybrid deep learning approach. Sensors, 2023, 23(14 6248

[13] Maaroufi MM, Stour L, Agoumi Aet al.. Motahhir S, Bossoufi Bet al.. The utilization of artificial intelligence and deep learning for identifying vehicle wheels. Digital technologies and applications, 2024, Cham, Springer461471 1098

[14] Ucar A, Karakose M, Kırımça N. Artificial intelligence for predictive maintenance applications: key components, trustworthiness, and future trends. Appl Sci, 2024, 142 898

[15] Cakir M, Ali Guvenc M, Mistikoglu S. The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Comput Ind Eng, 2021, 151 106948

[16] Cardoso D, Ferreira L. Application of predictive maintenance concepts using artificial intelligence tools. Appl Sci, 2021, 111 18

[17] Mahale Y, Kolhar S, More AS. A comprehensive review on artificial intelligence driven predictive maintenance in vehicles: technologies, challenges and future research directions. Discov Appl Sci, 2025, 74): 243

[18] Müller R, Leibundgut D, Stallaert B et al (2013) Validation of wheel maintenance measures on the rolling stock for reduced excitation of ground vibration. RIVAS Technical Report, SCP0-GA-2010-265754. Trafikverket, Borlänge, Sweden

[19] Costa BJA, Martins R, Santos Met al.. Weighing-in-motion wireless system for sustainable railway transport. Energy Procedia, 2017, 136: 408-413

[20] Brizuela J, Fritsch C, Ibez A. New ultrasonic techniques for detecting and quantifying railway wheel-flats. Reliability and safety in Railway, 2012, London, InTech399-418

[21] Deilamsalehy H, Havens TC, Lautala Pet al.. An automatic method for detecting sliding railway wheels and hot bearings using thermal imagery. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2017, 231(6): 690-700

[22] Traquinho N, Vale C, Ribeiro Det al.. Damage identification for railway tracks using onboard monitoring systems in in-service vehicles and data science. Machines, 2023, 11(10): 981

[23] Gordan M, Ghiasi R, Mosleh Aet al.. Blockchain-enabled data mining for pre-condition monitoring in railway infrastructure. J Nondestruct Test, 2024

[24] Fernández-Bobadilla HA, Bouchikhi Y, Martin U. GAN-based data augmentation of time series for fault diagnosis in railway track. Railw Eng Sci, 2025, 334642-683

[25] Ghiasi R, Malekjafarian A. Monitoring of railway tracks maintenance needs using dynamic responses collected by an in-service train. Railw Eng Sci, 2025, 33(4): 553-580

[26] Kundu P, Darpe AK, Singh SP et al (2018) A review on condition monitoring technologies for railway rolling stock. In: Proceedings of the PHM Society European Conference 2018, Philadelphia, PA, USA

[27] Mohammadi M, Mosleh A, Vale Cet al.. An unsupervised learning approach for wayside train wheel flat detection. Sensors, 2023, 23(4 1910

[28] Fu W, He Q, Feng Qet al.. Recent advances in wayside railway wheel flat detection techniques: a review. Sensors, 2023, 238): 3916

[29] Mosleh A, Ghiasi R, Gordan M, Ribeiro D, Malekjafarian A. Wayside monitoring for railway wheel flat identification: a multiclass supervised learning approach. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2025

[30] Jorge T, Magalhães J, Silva Ret al.. Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder. Veh Syst Dyn, 2025, 632232-257

[31] Mohammadi M, Mosleh A, Vale Cet al.. Smart railways: AI-based track-side monitoring for wheel flat identification. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2025, 239(3): 272-289

[32] Ngigi R, Pislaru C, Ball Aet al.. Modern techniques for condition monitoring of railway vehicle dynamics. J Phys Conf Ser, 2012, 364 012016

[33] Ye Y, Li H, Wang Qet al.. Fault diagnosis of railway wheelsets: a review. Measurement, 2025, 242 116169

[34] Asplund M, Lin J. Evaluating the measurement capability of a wheel profile measurement system by using GR&r. Measurement, 2016, 92: 19-27

[35] Asplund M, Palo M, Famurewa Set al.. A study of railway wheel profile parameters used as indicators of an increased risk of wheel defects. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2016, 230(2): 323-334

[36] RailCorp (2013) Engineering Standard Rolling Stock—Wheel Defect Manual ESR 0330. Version 1.2. RailCorp, Sydney

[37] Amini A, Entezami M, Huang Zet al.. Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals. Adv Mech Eng, 2016, 8(11 1687814016676000

[38] Zhang D, Entezami M, Stewart Eet al.. A novel Doppler Effect reduction method for wayside acoustic train bearing fault detection systems. Appl Acoust, 2019, 145: 112-124

[39] Yan G, Chen J, Bai Yet al.. A survey on fault diagnosis approaches for rolling bearings of railway vehicles. Processes, 2022, 104): 724

[40] Gupta P, Pradhan MK. Fault detection analysis in rolling element bearing: a review. Mater Today Proc, 2017, 422085-2094

[41] Soomro AA, Muhammad MB, Mokhtar AAet al.. Insights into modern machine learning approaches for bearing fault classification: a systematic literature review. Results Eng, 2024, 23 102700

[42] Zhang S, Zhang S, Wang Bet al.. Deep learning algorithms for bearing fault diagnostics—a comprehensive review. IEEE Access, 2020, 8: 29857-29881

[43] Peng X, Zeng J, Wang Jet al.. Wayside wheelset lateral motion detection and vehicle hunting instability evaluation. Meas Sci Technol, 2023, 34(12 125904

[44] Liang J, Sun J, Jiang Yet al.. Advances and challenges in the hunting instability diagnosis of high-speed trains. Sensors, 2024, 2417): 5719

[45] Ning J, Cui W, Chong Cet al.. Feature recognition of small amplitude hunting signals based on the MPE-LTSA in high-speed trains. Measurement, 2019, 131: 452-460

[46] Sun J, Meli E, Cai Wet al.. A signal analysis based hunting instability detection methodology for high-speed railway vehicles. Veh Syst Dyn, 2021, 59(10): 1461-1483

[47] Zheng B, Wei L, Zeng Jet al.. Experimental and numerical investigation of bogie hunting instability for railway vehicles based on multiple sensors. Sensors, 2024, 24(12): 4027

[48] Wichert A. Principles of quantum artificial intelligence, 20202Singapore, World Scientific

[49] Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev, 1958, 656386-408

[50] Martin TB, Talavage JJ. Application of neural logic to speech analysis and recognition. IEEE Trans Mil Electron, 1963, MIL-7(2–3): 189-196

[51] Kaushik A, Soni A, Soni R. A simple neural network approach to software cost estimation. Glob J Comput Sci, 2013, 13(1): 23-30

[52] Smith PD, Bull DR, Wykes C. Target classification with artificial neural networks using ultrasonic phased arrays. WIT Trans Inf Commun Technol, 1993, 1: 787-800

[53] Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inform Theory, 1967, 13(1): 21-27

[54] Werbos P (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. Dissertation, Harvard University

[55] Al-Mahasneh AJ, Anavatti SG, Garratt MA (2017) The development of neural networks applications from perceptron to deep learning. In: 2017 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), Surabaya, Indonesia, 12–14 October 2017. IEEE, pp 1–6

[56] Taye MM. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 2023, 12(5): 91

[57] Li Z, Liu F, Yang Wet al.. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst, 2022, 33126999-7019

[58] Daulay RSA, Efendi S. Suherman review of literature on improving the KNN algorithm. Trans Eng Comput Sci, 2023, 11: 63-72

[59] Vapnik VN. The nature of statistical learning theory information science and statistics, 1995, NY, Springer

[60] Cano Lengua MA, Papa Quiroz EA (2020) A systematic literature review on support vector machines applied to classification. In: 2020 IEEE Engineering International Research Conference (EIRCON), Lima, Peru, 21–23 October 2020

[61] Quinlan JR. Induction of decision trees. Mach Learn, 1986, 1(1): 81-106

[62] Safavian SR, Landgrebe D. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern, 1991, 21(3): 660-674

[63] Jain AK. Data clustering: 50 years beyond K-means. Pattern Recognit Lett, 2010, 31(8): 651-666

[64] Neha D, Vidyavathi BM. A survey on applications of data mining using clustering techniques. Int J Comput Appl, 2015, 126(2): 7-12

[65] Oyelade J, Isewon I, Oladipupo O et al (2019) Data clustering: algorithms and its applications. In: 19th International Conference on Computational Science and Its Applications (ICCSA), Saint Petersburg, Russia, July 1–4, 2019

[66] Arthur D, Vassilvitskii S (2007) K-means++: the advantages of careful seeding. In: SODA’07: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms, New Orleans, Louisiana, January 7–9, 2007. Society for Industrial and Applied Mathematics, PA, USA, pp 1027–1035

[67] Khan K, Rehman SU, Aziz K et al (2014) DBSCAN: past, present and future. In: The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Bangalore, India, February 17–19, 2014. IEEE, pp 232–238

[68] Jolliffe IT. Principal component analysis springer series in statistics, 2002, NY, Springe

[69] Sathiya Keerthi S, Ravindran B. A tutorial survey of reinforcement learning. Sadhana, 1994, 19(6): 851-889

[70] Mehta D, Uiet PU. State-of-the-art reinforcement learning algorithms. Int J Eng Res AND, 2020, 8(12): 717-722

[71] Tang R, De Donato L, Bes̆inović Net al.. A literature review of Artificial Intelligence applications in railway systems. Transp Res Part C Emerg Technol, 2022, 140 103679

[72] Hu C, Liu X (2016) Modeling track geometry degradation using support vector machine technique. In: 2016 Joint Rail Conference, Columbia, South Carolina, USA, April 12–15, 2016

[73] Lee JS, Hwang SH, Choi IY, Kim IK. Prediction of track deterioration using maintenance data and machine learning schemes. J Transp Eng Part A Syst, 2018, 144(9 04018045

[74] Lasisi A, Attoh-Okine N. Principal components analysis and track quality index: a machine learning approach. Transp Res Part C Emerg Technol, 2018, 91: 230-248

[75] Famurewa SM, Zhang L, Asplund M. Maintenance analytics for railway infrastructure decision support. J Qual Maint Eng, 2017, 233): 310-325

[76] Guedes A, Silva R, Ribeiro Det al.. Detection of wheel polygonization based on wayside monitoring and artificial intelligence. Sensors, 2023, 23(4 2188

[77] Mosleh A, Meixedo A, Ribeiro D et al (2021) Feature extraction and normalization for wheel flat detection on railway vehicles. In: International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII): Transferring Research into Practice, Porto, Portugal, 2021

[78] Mosleh A, Meixedo A, Ribeiro Det al.. Automatic clustering-based approach for train wheels condition monitoring. Int J Rail Transp, 2023, 115): 639-664

[79] Mosleh A, Meixedo A, Ribeiro Det al.. Machine learning approach for wheel flat detection of railway train wheels. Transp Res Procedia, 2023, 72: 4199-4206

[80] Mosleh A, Mohammadi M, Vale Cet al.. Smart detection of wheel defects using artificial intelligence and wayside monitoring system. Int J Rail Transp, 2023, 10(2): 9-18

[81] Silva R, Guedes A, Ribeiro Det al.. Early identification of unbalanced freight traffic loads based on wayside monitoring and artificial intelligence. Sensors, 2023, 23(3): 1544

[82] Su Z, Jamshidi A, Núñez Aet al.. Multi-level condition-based maintenance planning for railway infrastructures–a scenario-based chance-constrained approach. Transp Res Part C Emerg Technol, 2017, 84: 92-123

[83] Cardoso R, Cury A, Barbosa F. A robust methodology for modal parameters estimation applied to SHM. Mech Syst Signal Process, 2017, 95: 24-41

[84] Mosleh A, Meixedo A, Ribeiro D et al (2022) Intelligent clustering-based approach for railway wheel flat detection. In: Pombo J (Ed) Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance, Montpellier, France, August 22–25, 2022. Civil-Comp Press, Edinburgh, pp 1–6

[85] Gilmore JF, Elibiary KJ, Petersson LR (1992) AI in advanced traffic management systems: neural network system for traffic flow management. In: Applications of Artificial Intelligence. SPIE, Orlando, Florida, Proc SPIE 1709:558–571

[86] McKenzie P, Alder M (1993) Syntactic pattern recognition by quadratic neural nets: a case study of rail flaw classification. In: Proceedings of the International Conference on Neural Networks (IJCNN-93), Nagoya, Japan. IEEE

[87] Nefti S, Oussalah M (2004) A neural network approach for railway safety prediction. In: IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), The Hague, Netherlands. IEEE

[88] Sadeghi J, Askarinejad H. Application of neural networks in evaluation of railway track quality condition. J Mech Sci Technol, 2012, 26(1): 113-122

[89] Jamshidi A, Hajizadeh S, Su Zet al.. A decision support approach for condition-based maintenance of rails based on big data analysis. Transp Res Part C Emerg Technol, 2018, 95: 185-206

[90] Chen J, Liu Z, Wang Het al.. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network. IEEE Trans Instrum Meas, 2018, 67(2): 257-269

[91] Kang G, Gao S, Yu Let al.. Deep architecture for high-speed railway insulator surface defect detection: denoising autoencoder with multitask learning. IEEE Trans Instrum Meas, 2019, 68(8): 2679-2690

[92] Guler H. Prediction of railway track geometry deterioration using artificial neural networks: a case study for Turkish state railways. Struct Infrastruct Eng, 2014, 10(5): 614-626

[93] Oukhellou L, Debiolles A, Denœux Tet al.. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion. Eng Appl Artif Intell, 2010, 23(1): 117-128

[94] Zhang H, Yang J, Tao Wet al.. Vision method of inspecting missing fastening components in high-speed railway. Appl Opt, 2011, 50(20): 3658-3665

[95] Chen Z, Yang J, Chen Let al.. Efficient railway track region segmentation algorithm based on lightweight neural network and cross-fusion decoder. Autom Constr, 2023, 155 105069

[96] Tang Y, Qian Y. High-speed railway track components inspection framework based on YOLOv8 with high-performance model deployment. High Speed Railw, 2024, 2(1): 42-50

[97] Rahman FU, Ahmed MT, Hasan MMet al.. Real-time obstacle detection over railway track using deep neural networks. Procedia Comput Sci, 2022, 215: 289-298

[98] Zhang Z, Chen P, Huang Yet al.. Railway obstacle intrusion warning mechanism integrating YOLO-based detection and risk assessment. J Ind Inf Integr, 2024, 38100571

[99] Phusakulkajorn W, Núñez A et al (2023) Artificial intelligence in railway infrastructure: current research, challenges, and future opportunities. Intell Transp Infrastruct 2:liad016

[100] Simões ML, Vale C, Montenegro PA et al (2023) Influence of track and vehicle wheel damages in the train running safety. In: Proceedings of the XLIV Ibero-Latin American Congress on Computational Methods in Engineering, Porto, Portugal, 13–16 November, 2023

[101] Yang Y, Ling L, Wang Cet al.. Wheel/rail dynamic interaction induced by polygonal wear of locomotive wheels. Veh Syst Dyn, 2022, 60(1): 211-235

[102] Vale C. Wheel flats in the dynamic behavior of ballasted and slab railway tracks. Appl Sci, 2021, 11(15): 7127

[103] Bian J, Gu Y, Murray MH. A dynamic wheel–rail impact analysis of railway track under wheel flat by finite element analysis. Veh Syst Dyn, 2013, 516784-797

[104] Cui K, Qin X. Numerical computation of wheel–rail impact noises with considering wheel flats based on the boundary element method. J Vibroeng, 2016, 18(6): 3930-3940

[105] Nielsen JCO, Johansson A. Out-of-round railway wheels—a literature survey. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2000, 214(2): 79-91

[106] Zhai W, Jin X, Wen Zet al.. Wear problems of high-speed wheel/rail systems: observations, causes, and countermeasures in China. Appl Mech Rev, 2020, 72(6 060801

[107] Kanoje NK, Sharma SC, Harsha SP. EPFM analysis of subsurface crack beneath a wheel flat using dynamic condition. Procedia Mater Sci, 2014, 6: 43-60

[108] Kato T, Fujimura T, Hiramatsu Set al.. Subsurface crack propagation from internal defect in rolling contact fatigue of railway wheel steel. Mater Trans, 2021, 622): 185-190

[109] Chang C, Cai Y, Chen Bet al.. Experimental study of the wheel/rail impact caused by wheel flat within 400 km/h using full-scale roller rig. Railw Sci, 2022, 1(1): 76-89

[110] Prasad A, Jafferson JM. Finite element model of wheel–rail impact due to flat spot. Mater Today Proc, 2021, 46: 1221-1228

[111] Cai W, Chi M, Wu Xet al.. Effect of wheel initial state on the growth of polygonal wear on high-speed trains. Wear, 2023, 526–527 204894

[112] Bogacz R, Frischmuth K. On dynamic effects of wheel–rail interaction in the case of polygonalisation. Mech Syst Signal Process, 2016, 79: 166-173

[113] Australian Transport Safety Bureau (2022) Defective axle bearing leading to fire on passenger train SN68, Yerrinbool, New South Wales, on 13 October 2020. https://www.atsb.gov.au/publications/investigation_reports/2020/rair/ro-2020-017. Accessed 15 December 2024

[114] Gerdun V, Sedmak T, Šinkovec Vet al.. Failures of bearings and axles in railway freight wagons. Eng Fail Anal, 2007, 14(5): 884-894

[115] Sun J, Chi M, Jin Xet al.. Experimental and numerical study on carbody hunting of electric locomotive induced by low wheel–rail contact conicity. Veh Syst Dyn, 2021, 59(2): 203-223

[116] Hurtado-Hurtado G, Morales-Velazquez L, Valtierra-Rodríguez Met al.. Frequency analysis of the railway track under loads caused by the hunting phenomenon. Mathematics, 2022, 10(13): 2286

[117] Song I-H, Kim J-W, Koo J-Set al.. Modeling and simulation of collision-causing derailment to design the derailment containment provision using a simplified vehicle model. Appl Sci, 2020, 101118

[118] Shin JH, Park JH, Shin YJ. Development of hunting oscillation detection algorithm for railway vehicles by using accelerometers. Adv Mech Eng, 2024, 1611 16878132241296265

[119] Kowsalya M, Kalaimathi B, Alamelu Alias Rajasree S et al (2024) Smart system to detect under loading and overloading of wagons in transportation. In: 2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), Coimbatore, India, 28–29 June 2024

[120] Bragança C, Silva R, de Souza EFet al.. Unsupervised machine learning methodologies for identification of transversal imbalanced loads in freight railway vehicles. Railw Eng Sci, 2025, 334581-613

[121] Zheng Z, Yi C, Lin Jet al.. A novel deep learning architecture and its application in dynamic load monitoring of the vehicle system. Measurement, 2024, 229 114336

[122] Zhang D, Tang Y, Peng Qet al.. Effect of mass distribution on curving performance for a loaded wagon. Nonlinear Dyn, 2021, 104(3): 2259-2273

[123] Zhang D, Tang Y, Sun Zet al.. Optimising the location of wagon gravity centre to improve the curving performance. Veh Syst Dyn, 2022, 60(5): 1627-1641

[124] Vale C, Amate CS, Bonifácio C (2020) OPTIBOX - Software Tool for the OPTImal distribution of hot BOx aXle detectors. In: 6th International Conference on Road and Rail Infrastructure, Pula, Croatia, 20–22 May 2020. CETRA, pp 533–540

[125] Vale C, Bonifácio C, Seabra J et al (2016) Axle bearing condition monitoring, diagnosis and maintenance: the MAXBE project. In: Pombo J (Ed) Proceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance. Civil-Comp Press, Stirlingshire, UK, Paper 307

[126] Vale C, Bonifácio C, Seabra Jet al.. Novel efficient technologies in Europe for axle bearing condition monitoring–the MAXBE project. Transp Res Procedia, 2016, 14: 635-644

[127] Wayside Inspection Devices (WID) (2024) TBOGI-HD®: Bogie condition monitor and hunting detector for freight and heavy haul railways. https://www.wid.ca/tbogi-hd. Acessed 10 November 2024

[128] Apna TechnoloGies & Solutions (ApnaTech) (2025). https://apnatech.com. Accessed 10 November 2024

[129] Tarawneh C, Montalvo J, Wilson B. Defect detection in freight railcar tapered-roller bearings using vibration techniques. Railw Eng Sci, 2021, 29(1): 42-58

[130] LB-Foster (2024) Total Track Monitoring—WILD IV®. https://lbfoster.com/rail/total-track-monitoring/wild. Accessed 10 November 2024

[131] Selectra Vision (2024) Wheel measuring system. https://www.selectravision.com/wheels.php. Accessed 10 November 2024

[132] Voestalpine voestalpine Railway Systems (2024) PHOENIX® HBD/HWD Hot Box and Hot Wheel Detection. https://www.voestalpine.com/railway-systems/en/products/phoenixmds-hbd-hwd-hot-box-detection-and-hot-wheel-detection/. Accessed 10 November 2024

[133] Ibérica Tecnológica En Sistemas De Seguridad Ferroviarios, Sl, (ITSS (2024) Pegasus Hot Wheel Detector. https://www.itss.tech/pegasus-habd-hwd. Accessed 10 November 2024

[134] MERMEC Group (2024) Train monitoring. https://www.mermecgroup.com/15/measuring-trains-and-systems.php. Accessed 10 November 2024

[135] Track IQ RailBAM®Wbtec Corporation (2024) Wayside Condition Monitoring System. https://www.trackiq.net/. Accessed 10 November 2024

[136] PSI Technics (2024) Automated Train Inspection System. https://www.psi-technics.com/EN/ServicesProducts/Automated-TrainInspection/automated-train-inspectioninspect.php. Accessed 10 November 2024

[137] Southern Technologies Corporation (2024) SMARTSCAN NG®. https://www.southern-tech.com/products/item/smartscan-ng. Accessed 10 November 2024

[138] WABTEC Corporation (2024) KinetiX Inspection Technologies. https://www.wabteccorp.com/digital-intelligence/condition-monitoring/kinetix-inspection-technologies/wayside. Accessed 10 November 2024

[139] Transportation Technological Center, Inc. (TTCI) (2024) TADS® Trackside Acoustic Detection System. https://ttc-ensco.com. Accessed 10 November 2024

[140] Hitachi Rail STS (2024) WIM & WILD system. https://www.scribd.com/document/376737477/Asts-Hitachi-Wim-Wild-Lr. Accessed 10 November 2024

[141] Hottinger Brüel & Kjær (2024) Wayside Train Measurement. https://www.hbkworld.com/en/solutions/industries/rail. Accessed 10 November 2024

[142] ARTC (2024) Wayside Monitoring Systems. https://www.artc.com.au/customers/wayside/. Accessed 10 November

[143] Nagory Foster Private Limited (2024) Hunting Truck Detector. http://www.nagoryfoster.com/Hunting_Truck_Detector.asp. Accessed 10 November

[144] Trimble rail solutions, Trimble (2024) C2M rail asset management system. https://rail.trimble.com/c2m/. Accessed 10 November

[145] Stratman B, Liu Y, Mahadevan S. Structural health monitoring of railroad wheels using wheel impact load detectors. J Fail Anal Prev, 2007, 7(3): 218-225

[146] Krummenacher G, Ong CS, Koller Set al.. Wheel defect detection with machine learning. IEEE Trans Intell Transp Syst, 2018, 19(4): 1176-1187

[147] Wei CL, Lai C-C, Liu S-Yet al.. A fiber Bragg grating sensor system for train axle counting. IEEE Sens J, 2010, 10(12): 1905-1912

[148] Liu XZ, Xu C, Ni YQ. Wayside detection of wheel minor defects in high-speed trains by a Bayesian blind source separation method. Sensors, 2019, 19(18): 3981

[149] Filograno ML, Corredera Guillen P, Rodriguez-Barrios Aet al.. Real-time monitoring of railway traffic using fiber Bragg grating sensors. IEEE Sens J, 2012, 12185-92

[150] Mishra S, Sharan P, Saara K. Real time implementation of fiber Bragg grating sensor in monitoring flat wheel detection for railways. Eng Fail Anal, 2022, 138 106376

[151] Hill D (2015) Distributed acoustic sensing (DAS): theory and applications. In: Frontiers in Optics 2015. OSA Technical Digest (online) (Optica Publishing Group, 2015), paper FTh4E.1

[152] Gao L, Zhou C, Xiao Het al.. Continuous vertical wheel–rail force reconstruction method based on the distributed acoustic sensing technology. Measurement, 2022, 197 111297

[153] Peng F, Duan N, Rao YJet al.. Real-time position and speed monitoring of trains using phase-sensitive OTDR. IEEE Photon Technol Lett, 2014, 26(20): 2055-2057

[154] Zhang G, Song Z, Osotuyi AGet al.. Railway traffic monitoring with trackside fiber-optic cable by distributed acoustic sensing technology. Front Earth Sci, 2022, 10 990837

[155] Vukušičová D. Analysis of dynamic effects acting on railway crossings. Acta Polytech CTU Proc, 2017, 11: 97-102

[156] Belotti V, Crenna F, Michelini RCet al.. Wheel–flat diagnostic tool via wavelet transform. Mech Syst Signal Process, 2006, 20(8): 1953-1966

[157] Salehi M, Bagherzadeh SA, Fakhari M. Experimental detection of train wheel defects using wayside vibration signal processing. Struct Health Monit, 2023, 22(5): 3286-3301

[158] Avsievich A, Avsievich V, Avsievich Net al.. Railway track stress–strain analysis using high-precision accelerometers. Appl Sci, 2021, 11(24): 11908

[159] Salzburger HJ, Schuppmann M, Wang Let al.. In-motion ultrasonic testing of the tread of high-speed railway wheels using the inspection system AUROPA III. Insight Non-Destr Test Cond Monit, 2009, 517370-372

[160] Brizuela J, Fritsch C, Ibáñez A. Railway wheel-flat detection and measurement by ultrasound. Transp Res Part C Emerg Technol, 2011, 19(6): 975-984

[161] Brizuela J, Ibañez A, Nevado Pet al.. Railway wheels flat detector using Doppler effect. Phys Procedia, 2010, 3(1): 811-817

[162] Sun Z, Lu J. An ultrasonic signal denoising method for EMU wheel trackside fault diagnosis system based on improved threshold function. IEEE Access, 2021, 9: 96244-96256

[163] Montinaro N, Epasto G, Cerniglia Det al.. Laser ultrasonics inspection for defect evaluation on train wheel. NDT E Int, 2019, 107 102145

[164] Papaelias M, Huang Z, Amini A et al (2014) Advanced wayside condition monitoring of rolling stock wheelsets. In: 11th European Conference on Non-Destructive Testing (ECNDT 2014), Prague, Czech Republic, 6–10 October 2014

[165] Kukėnas V, Jasevičius R, Petrenko Vet al.. Analysis of sound power level changes caused by wheel with flat spot using numerical and physical experiment. Appl Sci, 2021, 11(5 2141

[166] He Q, Wang J, Hu Fet al.. Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement. J Sound Vib, 2013, 33221): 5635-5649

[167] Liu Y, Qian Q, Liu Fet al.. Wayside acoustic fault diagnosis of train wheel bearing based on Doppler effect correction and fault-relevant information enhancement. Adv Mech Eng, 2017, 9(11 168781401773267

[168] Chen L, Choy YS, Wang TGet al.. Fault detection of wheel in wheel/rail system using kurtosis beamforming method. Struct Health Monit, 2020, 192): 495-509

[169] Komorski P, Szymanski GM, Nowakowski Tet al.. Advanced acoustic signal analysis used for wheel-flat detection. Lat Am J Solids Struct, 2021, 18 e338

[170] Elforjani M, Mba D. Accelerated natural fault diagnosis in slow speed bearings with acoustic emission. Eng Fract Mech, 2010, 77(1): 112-127

[171] Bollas K, Papasalouros D, Kourousis Det al.. Acoustic emission monitoring of wheel sets on moving trains. Constr Build Mater, 2013, 48: 1266-1272

[172] Gao Y, Feng Q, Cui J. A simple method for dynamically measuring the diameters of train wheels using a one-dimensional laser displacement transducer. Opt Lasers Eng, 2014, 53: 158-163

[173] Zheng F, Zhang B, Gao Ret al.. A high-precision method for dynamically measuring train wheel diameter using three laser displacement transducers. Sensors, 2019, 19(19): 4148

[174] Ran Y, He Q, Feng Qet al.. High-accuracy on-site measurement of wheel tread geometric parameters by line-structured light vision sensor. IEEE Access, 2021, 9: 52590-52600

[175] Sun J, Zhang Z, Zhang J. Reconstructing normal section profiles of 3-D revolving structures via pose-unconstrained multiline structured-light vision. IEEE Trans Instrum Meas, 2021, 70: 1-12

[176] Merainani B, Toullier T, Dumoulin J (2023) Moving train wheel axles automated detection, counting, and tracking by combining AI with Kalman filter applied to thermal infrared image sequences. In: Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, Munich, Germany, 26–30 June 2023

[177] Kim M-S, Oh S-C, Kim G-Y et al (2014) Underbody component monitoring system of railway vehicles using the infra-red thermal images. In: 2014 International SoC Design Conference (ISOCC), Ramada Plaza, Jeju Island, Korea, 3–6 November 2014

[178] Jakubek B, Grochalski K, Rukat Wet al.. Thermovision measurements of rolling bearings. Measurement, 2022, 189 110512

[179] Emoto T, Ravankar AA, Ravankar Aet al.. Automatic inspection of wheel surface defects using a combination of laser sensors and machine vision. SICE J Control Meas Syst Integr, 2024, 17(1): 57-66

[180] Li H, Wang Y, Zeng Jet al.. Virtual point tracking method for online detection of relative wheel–rail displacement of railway vehicles. Reliab Eng Syst Saf, 2024, 246 110087

[181] Milković D, Simić G, Jakovljević Žet al.. Wayside system for wheel–rail contact forces measurements. Measurement, 2013, 46(9): 3308-3318

[182] Lunys O, Dailydka S, Steišūnas Set al.. Analysis of freight wagon wheel failure detection in Lithuanian railways. Procedia Eng, 2016, 134: 64-71

[183] Alemi A, Corman F, Pang Yet al.. Reconstruction of an informative railway wheel defect signal from wheel–rail contact signals measured by multiple wayside sensors. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2019, 233(1): 49-62

[184] Cortis D, Bruner M, Malavasi G. Development of a wayside measurement system for the evaluation of wheel–rail lateral contact force. Measurement, 2020, 159 107786

[185] Zhou C, Gao L, Xiao Het al.. Railway wheel flat recognition and precise positioning method based on multisensor arrays. Appl Sci, 2020, 10(4): 1297

[186] Zhou W, Abdulhakeem S, Fang Cet al.. A new wayside method for measuring and evaluating wheel–rail contact forces and positions. Measurement, 2020, 166 108244

[187] Ni YQ, Zhang QH. A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring. Struct Health Monit, 2021, 2041536-1550

[188] Mosleh A, Montenegro P, Alves Costa Pet al.. An approach for wheel flat detection of railway train wheels using envelope spectrum analysis. Struct Infrastruct Eng, 2021, 17(12): 1710-1729

[189] Pintão B, Mosleh A, Vale Cet al.. Development and validation of a weigh-in-motion methodology for railway tracks. Sensors, 2022, 22(5 1976

[190] Peng X, Zeng J, Wang Jet al.. Wayside wheel–rail vertical contact force continuous detecting method and its application. Measurement, 2022, 193 110975

[191] Lourenço A, Ferraz C, Ribeiro Det al.. Adaptive time series representation for out-of-round railway wheels fault diagnosis in wayside monitoring. Eng Fail Anal, 2023, 152 107433

[192] Wei C, Xin Q, Chung WHet al.. Real-time train wheel condition monitoring by fiber Bragg grating sensors. Int J Distrib Sens Netw, 2012, 81 409048

[193] Lan C, Yang Z, Liang Xet al.. Experimental study on wayside monitoring method of train dynamic load based on strain of ballastless track slab. Constr Build Mater, 2023, 394 132084

[194] Suhaimi NS, Ahmad MIM, Nuawi MZet al.. Structural condition assessment based strain–stress behaviour for railway welded rail joint using rosette fibre Bragg grating optical sensor. Results Eng, 2023, 19 101300

[195] Gao R, He Q, Feng Qet al.. In-service detection and quantification of railway wheel flat by the reflective optical position sensor. Sensors, 2020, 2017): 4969

[196] Liu F, Liang L, Hou Cet al.. On-machine measurement of wheel tread profile with the 1-D laser sensor. IEEE Trans Instrum Meas, 2021, 70: 1-11

[197] Skarlatos D, Karakasis K, Trochidis A. Railway wheel fault diagnosis using a fuzzy-logic method. Appl Acoust, 2004, 65(10): 951-966

[198] Nowakowski T, Komorski P, Szymański GMet al.. Wheel-flat detection on trams using envelope analysis with Hilbert transform. Lat Am J Solids Struct, 2019, 16 e148

[199] Mosleh A, Meixedo A, Ribeiro Det al.. Early wheel flat detection: an automatic data-driven wavelet-based approach for railways. Veh Syst Dyn, 2023, 61(6): 1644-1673

[200] Gonçalves V, Mosleh A, Vale Cet al.. Wheel out-of-roundness detection using an envelope spectrum analysis. Sensors, 2023, 23(4 2138

[201] Cai W, Wu X, Chi Met al.. Wheel polygonisation growth due to multiple wheelsets/track coupling vibration. Veh Syst Dyn, 2023, 611): 177-199

[202] Aktas M, Gunel EH, Yilmazer P et al (2018) Detection of wheel flatten defect on the moving train with acoustic emission sensor. In: 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Bologna, Italy, 9–12 September 2018

[203] Chang Y, Zhang X, Lin Cet al.. An efficient method for wheel-flattened defects detection based on acoustic emission technique. IEEE Trans Ultrason Ferroelectr Freq Control, 2022, 69(2): 843-853

[204] Dybała J, Radkowski S. Reduction of Doppler effect for the needs of wayside condition monitoring system of railway vehicles. Mech Syst Signal Process, 2013, 38(1): 125-136

[205] Hu F, Shen CQ, Kong FR. De-noising method for wayside train bearings acoustic signal based on Doppler shift. Appl Mech Mater, 2013, 333–335: 504-509

[206] Cheng X, Chen Y, Xing Zet al.. A novel online detection system for wheelset size in railway transportation. J Sensors, 2016, 2016 9507213

[207] Xing Z, Chen Y, Wang Xet al.. Online detection system for wheel-set size of rail vehicle based on 2D laser displacement sensors. Optik, 2016, 127(4): 1695-1702

[208] Pan X, Liu Z, Zhang G. Reliable and accurate wheel size measurement under highly reflective conditions. Sensors, 2018, 18(12): 4296

[209] Tarawneh C, Aranda J, Hernandez Vet al.. An investigation into wayside hot-box detector efficacy and optimization. Int J Rail Transp, 2020, 83264-284

[210] Pan X, Liu Z, Zhang G. On-site reliable wheel size measurement based on multisensor data fusion. IEEE Trans Instrum Meas, 2019, 68(11): 4575-4589

[211] Yang K, Peng J, Gao X et al (2016) Research on the detecting method of CRH wheel flat at low speed. In: 2016 18th International Wheelset Congress (IWC), Chengdu, China, November 7–10, 2016

[212] Barman J, Hazarika D. Linear and quadratic time–frequency analysis of vibration for fault detection and identification of NFR trains. IEEE Trans Instrum Meas, 2020, 69(11): 8902-8909

[213] Mosleh A, Montenegro PA, Costa PAet al.. Railway vehicle wheel flat detection with multiple records using spectral kurtosis analysis. Appl Sci, 2021, 11(9 4002

[214] Gonçalves V, Mosleh A, Vale C et al (2023) Envelope spectrum analysis with algorithm simulations to detect railway wheel out-of-roundness defects. In: The XLIV Ibero-Latin American Congress on Computational Methods in Engineering, Porto, Portugal, 3–16 November 2023

[215] Cao WJ, Zhang S, Bertola NJet al.. Time series data interpretation for ‘wheel-flat’ identification including uncertainties. Struct Health Monit, 2023, 221): 3-18

[216] Alemi A, Corman F, Pang Yet al.. Evaluation of the influential parameters contributing to the reconstruction of railway wheel defect signals. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2020, 234(9): 1005-1016

[217] Gao R, He Q, Feng Q. Railway wheel flat detection system based on a parallelogram mechanism. Sensors, 2019, 19(16 3614

[218] Deilamsalehy H, Havens TC, Lautala P (2016) Detection of sliding wheels and hot bearings using wayside thermal cameras. In: ASME/IEEE Joint Rail Conference, Columbia, South Carolina, US, 12–15 April 2016

[219] Deilamsalehy H, Havens TC, Lautala P (2017) Sensor fusion of wayside visible and thermal imagery for rail car wheel and bearing damage detection. In: ASME/IEEE Joint Rail Conference, Philadelphia, Pennsylvania, USA, 4–7 April 2017

[220] Ghiasi R, Gordan M, Mosleh A et al (2024) M-CLUSTER: multistage clustering for unsupervised train wheel condition monitoring. Veh Syst Dyn:1–26

[221] Rombach K, Michau G, Ratnasabapathy Ket al.. Contrastive feature learning for fault detection and diagnostics in railway applications. Available at SSRN, 2022, 4213498: 2022

[222] Rombach K, Michau G, Bürzle Wet al.. Learning informative health indicators through unsupervised contrastive learning. IEEE Trans Rel, 2025, 741): 2408-2420

[223] Peng C, Gao X, Zhao B et al (2016) Way-side wheel crack detecting using arrayed ultrasonic probes. In: 2016 18th International Wheelset Congress (IWC), Chengdu, China, 7–10 November 2016

[224] Zhang Q, Peng J, Tian Ket al.. Advancing ultrasonic defect detection in high-speed wheels via UT-YOLO. Sensors, 2024, 24(5): 1555

[225] Cline JE, Bilodeau JR, Smith RL (1998) Acoustic wayside identification of freight car roller bearing defects. In: Proceedings of the 1998 ASME/IEEE joint railroad conference, Philadelphia, PA, USA, 16–16 April 1998. IEEE, pp 79–83

[226] Zhang A, Liu F, Kong FR. Doppler distortion removal method for multiple acoustic sources. Appl Mech Mater, 2013, 373374/375874-879

[227] Liu Y, Qian Q, Fu Y et al (2016) Wayside acoustic fault diagnosis of railway wheel-bearing paved with Doppler Effect reduction and EEMD-based diagnosis information enhancement. In: 2016 10th International Conference on Sensing Technology (ICST), Nanjing, China, 11–13 November 2016

[228] Zhang H, Zhang S, He Qet al.. The Doppler effect based acoustic source separation for a wayside train bearing monitoring system. J Sound Vib, 2016, 361: 307-329

[229] Wang C, Shen C, He Qet al.. Wayside acoustic defective bearing detection based on improved Dopplerlet transform and Doppler transient matching. Appl Acoust, 2016, 101: 141-155

[230] Zhang S, He Q, Ouyang Ket al.. Multi-bearing weak defect detection for wayside acoustic diagnosis based on a time-varying spatial filtering rearrangement. Mech Syst Signal Process, 2018, 100: 224-241

[231] Zhang D, Entezami M, Stewart Eet al.. Adaptive fault feature extraction from wayside acoustic signals from train bearings. J Sound Vib, 2018, 425: 221-238

[232] Zhang D, Entezami M, Stewart Eet al.. Wayside acoustic detection of train bearings based on an enhanced spline-kernelled chirplet transform. J Sound Vib, 2020, 480 115401

[233] Liu F, Wu R, Teng Fet al.. A two-stage learning model for track-side acoustic bearing fault diagnosis. IEEE Trans Instrum Meas, 2021, 70: 1-12

[234] Liu D, Hu D, Shi Wet al.. A successive sources deletion method for enhancing the trackside acoustic weak signal of axle bearing. Measurement, 2023, 223 113744

[235] He S, Hu D, Yu Get al.. Trackside acoustic detection of axle bearing fault using wavelet domain moving beamforming method. Appl Acoust, 2022, 195 108851

[236] Shi M, Liu X, Wei Wet al.. Wayside acoustic fault diagnosis of train bearing based on improved linear discriminant analysis. IEEE Sens J, 2022, 22(14): 14228-14236

[237] Dias URF, Vargas e Pinto AC, Monteiro HLMet al.. New perspectives for the intelligent rolling stock classification in railways: an artificial neural networks-based approach. J Braz Soc Mech Sci Eng, 2024, 46(4 230

[238] Vale C (2014) Advanced approaches for axle bearing condition monitoring. In: Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, Ajaccio, Corsica, France, January 2014

[239] Yang S, Gu X, Liu Yet al.. A general multi-objective optimized wavelet filter and its applications in fault diagnosis of wheelset bearings. Mech Syst Signal Process, 2020, 145 106914

[240] Pan J, Li W, Dai X (2013) Train overload and unbalanced load detection based on FBG gauge. In: Fourth Asia Pacific Optical Sensors Conference 2013, Wuhan, China.

[241] Allotta B, Gaburri G, Innocenti A et al (2014) An innovative high speed Weigh in Motion system for railway vehicles. In: 2014 IEEE/ASME 10th International Conference on Mechatronic and Embedded Systems and Applications (MESA, Senigallia, Italy, 10–12 September 2014

[242] Mosleh A, Alves Costa P, Calçada R (2017) Estimation of static load due to weight in motion system. In: International Conference on Contemporary Iran in Civil Engineering, Architecture and Urban Development, Tehran, Iran, August 16, 2017

[243] Mosleh A, Costa PA, Calçada R. A new strategy to estimate static loads for the dynamic weighing in motion of railway vehicles. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2020, 2342): 183-200

[244] Mosleh A, Meixedo A, Costa P et al (2019) Trackside monitoring solution for weighing in motion of rolling stock. In: Proceedings of the TESTE2019—2nd Conference on Testing and Experimentations in Civil Engineering, Porto, Portugal, 19–21 February 2019

[245] Pintão B, Mosleh A, Vale C (2022) Dynamic loads in ballasted tracks: a parametric study for defining a WIM system. In: Proceedings of the 3rd Conference on Testing and Experimentation in Civil Smart Technologies, Lisbon, Portugal, 21–23 Febrary 2022.

[246] Vale C, Calçada R. Dynamic response of a coupled vehicle–track system to real longitudinal rail profiles. Proc Tenth Int Conf Comput Struct Technol, 2010, 93: 11

[247] Ding Y, Cheng LR (2018) High speed overload and imbalance load system in China. In: ASME/IEEE Joint Rail Conference, Pittsburgh, Pennsylvania, USA, 18–20 April 2018

[248] Wang Q, Li D, Zeng Jet al.. A diagnostic method of freight wagons hunting performance based on wayside hunting detection system. Measurement, 2024, 227 114274

[249] Asplund M, Söderström P. Detector response from a defective wheel. Wear, 2024, 542–543 205282

[250] Asplund M, Famurewa S, Rantatalo M. Condition monitoring and e-maintenance solution of railway wheels. J Qual Maintain Eng, 2014, 20(3): 216-232

[251] Asplund M, Gustafsson P, Nordmark Tet al.. Reliability and measurement accuracy of a condition monitoring system in an extreme climate: a case study of automatic laser scanning of wheel profiles. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2014, 228(6): 695-704

[252] Asplund M, Rantatalo M. Evaluation of wheel profile measurements by means of the contact-point function for the wheel–rail interface. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2018, 232(4): 1225-1239

[253] Kılınç O, Vágner J. Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside. Vib Proced, 2017, 11: 13-18

[254] Kilinc O, Vágner J (2022) Condition monitoring of metro wheelsets using wayside vibration and acoustic sensors. Available at SSRN 4170145

[255] Guo G, Peng J, Yang K et al (2017) Wheel tread defects inspection based on SVM. In: 2017 Far East NDT New Technology & Application Forum (FENDT), Xi’an, 22–24 June 2017

[256] Xing Z, Zhang Z, Yao Xet al.. Rail wheel tread defect detection using improved YOLOv3. Measurement, 2022, 203 111959

[257] Trilla A, Bob-Manuel J, Lamoureux B et al (2021) Integrated multiple-defect detection and evaluation of rail wheel tread images using convolutional neural networks. Int J Progn Health Manag 12(1)

[258] Liu S, Wang Q, Luo Y. A review of applications of visual inspection technology based on image processing in the railway industry. Transp Saf Environ, 2019, 1(3): 185-204

[259] Zhao X, Ma F, Güera D et al (2022) Generative multiplane images: making a 2D gan 3D-aware. In: European Conference on Computer Vision, Tel Aviv, Israel, October 23–27, 2022

[260] Zhai G, Xu Y, Spencer BF. Bidirectional graphics-based digital twin framework for quantifying seismic damage of structures using deep learning networks. Struct Health Monit, 2025, 24(1): 86-110

[261] Zhai G, Yao Z, Wang D et al (2026) Autonomous post-earthquake structural assessment based on bidirectional graphics-based digital twin (Bi-GBDT) with physical and visual realism. Adv Eng Inform 69: 103971

Funding

FCT - Fundação para a Ciência e Tecnologia(2021.04272.CEECIND)

FCT – Fundação para a Ciência e Tecnologia(2024.02919.BD)

The authors want to acknowledge UIDB/04708/2020 with DOI 10.54499/UIDB/04708/2020 (https://doi.org/10.54499/UIDB/04708/2020) and Programmatic Funding - UIDP/04708/2020 with DOI 10.54499/UIDP/04708/2020 (https://doi.org/10.54499/UIDP/04708/2020) of the CON

NRPCES

RIGHTS & PERMISSIONS

The Author(s)