Fault Detection and Diagnostic Methods for Railway Systems – A Literature Survey

• Autorzy: Wróbel Jakub , Bury Paweł , Zając Mateusz , Kierzkowski Artur , Tubek Sławomir , Blaut Jędrzej

Identyfikatory

DOI

0.12913/22998624/191762

• Języki publikacji: EN

Abstrakty

EN

This paper presents a systematic literature survey on diagnostic methods used for railway materials and systems. The authors analyze various railway accident reports, focusing on the types of failures described and their causes. Previous review papers have addressed various aspects of railway systems diagnostics; however, most of the existing research focuses on specific parts of the rail vehicle or subsystems. In contrast, this survey focuses on railway diagnostic systems rather than general diagnostic methods used in mechanical and electrical engineering. The authors classify the types of failures and diagnostic methods that are used in rail transport into two categories: infrastructure and rolling stock. The purpose of this paper is to systematize the types of failure that occur in railway transport systems; identify the state-of-the-art means and methods of diagnostics in railway materials and systems, with particular focus on new research findings; and identify trends and possible research gaps in need of further development.

Słowa kluczowe

EN

railway; diagnostics; failure detection; rolling stock; railway track; railway wheel; railway bogie; railway failure

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 6
• Strony: 361--391
• Opis fizyczny: Bibliogr. 110 poz., fig., tab.

Twórcy

autor

Wróbel Jakub

• Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, ul. Łukasiewicza 7/9, Wroclaw, Poland

• https://orcid.org/0000-0001-6676-0051

autor

Bury Paweł

• Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, ul. Łukasiewicza 7/9, Wroclaw, Poland

• https://orcid.org/0000-0002-1480-0653

autor

Zając Mateusz

• Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, ul. Łukasiewicza 7/9, Wroclaw, Poland

• https://orcid.org/0000-0001-7199-9163

autor

Kierzkowski Artur

• Department of Technical Systems Operation and Maintenance, Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, ul. Łukasiewicza 7/9, Wroclaw, Poland

• https://orcid.org/0000-0003-2052-2167

autor

Tubek Sławomir

• Tankwagon Sp. z o.o., Św. Ducha 5A/15 Str., Szczecin, Poland

autor

Blaut Jędrzej

• Department of Mechanical Engineering and Robotics, AGH University of Krakow, al. A. Mickiewicza 30, Krakow, Poland

• https://orcid.org/0000-0002-9638-1287

Bibliografia

• 1. U.S. Department Of Transportation, Federal Railroad Administration Office of Safety Analysis, Ten Year Accident/Incident Overview. [Online]. Dostępne na: https://safetydata.fra.dot.gov/OfficeofSafety/publicsite/Query/TenYearAccidentIncidentOverview.aspx
• 2. Strano S., Terzo M. Review on model-based methods for on-board condition monitoring in railway vehicle dynamics. Advances in Mechanical Engineering 2019; 11(2): 1687814019826795. doi:10.1177/1687814019826795.
• 3. Bernal E., Spiryagin M., Cole C. Onboard condition monitoring sensors, systems and techniques for freight railway vehicles: a review. IEEE Sensors Journal 2019, 19(1): 4–24, doi: 10.1109/JSEN.2018.2875160.
• 4. Muthukumar M., Nallathambi S. Remote sensor networks for condition monitoring: An application on railway industry. In IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Karur: IEEE, 2017, s. 1–5. doi: 10.1109/ICEICE.2017.8191894.
• 5. Tsunashima H. Condition monitoring of railway tracks from car-body vibration using a machine learning technique. Applied Sciences 2019; 9(13), doi: 10.3390/app9132734.
• 6. Chia L., Bhardwaj B., Lu P., Bridgelall R. Railroad track condition monitoring using inertial sensors and digital signal processing: a review. IEEE Sensors J. 2019; 19(1): 25–33. doi: 10.1109/JSEN.2018.2875600.
• 7. Hamadache M., Dutta S., Olaby O., Ambur R., Stewart E., Dixon R. On the fault detection and diagnosis of railway switch and crossing systems: an overview. Applied Sciences 2019; 9(23): 5129, doi: 10.3390/app9235129.
• 8. Renfrew A.C. Condition monitoring of railway electric point machines. IEE Proceedings - Electric Power Applications 2002; 149(6): 465–473(8).
• 9. Oyebande B.O., Renfrew A.C. The design and implementation of a simulated trackside environment test system for an electric point machine. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2007; 221(4): 477–485, doi: 10.1243/09544097F02605.
• 10. Network Rail. Broken rail explained. https://www.networkrail.co.uk/stories/broken-rail-explained/
• 11. Network Rail. Buckled rail. https://www.n e t w o r k r a i l . c o . u k / r u n n i n g - t h e - r a i l w a y /looking-after-the-railway/delays-explained/buckled-rail-and-summer-heat/
• 12. Bogue R.W. University spin‐off company launches novel structural stress monitoring system. Sensor Review 2003; 23(3): 207–208. doi:10.1108/02602280310481823.
• 13. Kojima T., Tsunashima H., Matsumoto A. Fault detection of railway track by multi-resolution analysis. In 4th International Conference on Wavelet Analysis and Its Applications, Macau, China 2005. IEEE Macau Section: Macau.
• 14. Kojima T., Tsunashima H., Matsumoto A. Fault detection of railway track by multi-resolution analysis. Computers in Railways X 2006; 88. doi:10.2495/CR060931.
• 15. Weston P.F., Ling C.S., Goodman C.J., Roberts C., Li P., Goodall R.M. Monitoring lateral track irregularity from in-service railway vehicles. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2007; 221(1): 89–100, doi: 10.1243/0954409JRRT64.
• 16. Naderi H., Mirabadi A. Railway track condition monitoring using FBG and FPI fiber optic sensors. In IET International Conference on Railway Condition Monitoring, Birmingham, UK: IEE, 2006, 198–203. doi: 10.1049/ic:20060069.
• 17. Hayashi Y., Kojima T., Tsunashima H., Marumo Y. Real time fault detection of railway vehicles and tracks. In IET International Conference on Railway Condition Monitoring, Birmingham, UK: IEE, 2006, 20–25. doi: 10.1049/ic:20060037.
• 18. Westeon P.F., Ling C.S., Roberts C., Goodman C.J., Li P., Goodall R.M. Monitoring vertical track irregularity from in-service railway vehicles. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit2007; 221(1), 75–88, doi: 10.1243/0954409JRRT65.
• 19. Attivissimo F., Danese A., Giaquinto N., Sforza P. A Railway Measurement System to Evaluate the Wheel–Rail Interaction Quality. IEEE Trans. Instrum. Meas 2007; 56(5): 1583–1589, doi: 10.1109/TIM.2007.903583.
• 20. Ho S.L. Futuristic railway condition monitoring system. In IET International Conference on Railway Engineering 2008 (ICRE 2008), Hong Kong, China: IEE, 271–279. doi: 10.1049/ic:20080049.
• 21. Mizuno Y., Fujino Y., Kataoka K., Matsumoto Y. Development of a mobile sensing unit and its prototype implementation. Tinshhua Sci. Technol 2008; 13(S1): 223–227. doi: 10.1016/S1007-0214(08)70153-6.
• 22. Mori H., Tsunashima H., Kojima T., Matsumoto A., Mizuma T. Condition monitoring of railway track using in-service vehicle. JMTL 2010; 3(1): 154–165, doi: 10.1299/jmtl.3.154.
• 23. Ward C.P. Condition monitoring opportunities using vehicle-based sensors. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2011; 225(2): 202–218. doi: 10.1177/09544097JRRT406.
• 24. Lee J.S., Choi S., Kim S.S, Kim Y.G., Kim S.W., Park C. Track condition monitoring by in-service trains: a comparison between axle-box and bogie accelerometers. In 5th IET Conference on Railway Condition Monitoring and Non-Destructive Testing (RCM 2011), Derby, UK: IET, 2011, 3A1-3A1. doi: 10.1049/cp.2011.0586.
• 25. Lee J.S., Choi S., Kim S.S., Park C., Kim Y.G. A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie. IEEE Trans. Instrum. Meas 2012; 61(3): 749–758, doi: 10.1109/TIM.2011.2170377.
• 26. Tesfa B., Horler G., Thobiani F.A., Gu F., Ball A.D. A clamping force measurement system for monitoring the condition of bolted joints on railway track joints and points. J. Phys. Conf. Ser. 2012; 364(012021). doi:10.1088/1742-6596/364/1/012021.
• 27. Ngigi R.W., Pislaru C., Ball A., Gu F. Modern techniques for condition monitoring of railway vehicle dynamics. J. Phys.: Conf. Ser 2012; 364: 012016. doi: 10.1088/1742-6596/364/1/012016.
• 28. Chellaswamy C., Akila V., Dinesh Babu A., Arasan N.K. Fuzzy logic based railway track condition monitoring system. In IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN) 2013, Tirunelveli: IEEE, 250–255. doi: 10.1109/ICE-CCN.2013.6528503.
• 29. Bagshawe M.J. Investigating inertial measurements using MEMS devices in trainborne automatic track condition monitoring applications. In IET Conference on Control and Automation 2013: Uniting Problems and Solutions, Birmingham, UK: Institution of Engineering and Technology, s. 33–33. doi: 10.1049/cp.2013.0010.
• 30. Chellaswamy C., Aishwarya R., Murthy S.L.N., Vigneshwar C. Green controller for rail track condition monitoring and information system. In International Conference on Green Computing, Communication and Conservation of Energy (ICGCE) 2013, CHENNAI, India: IEEE, 2013, 562–567. doi: 10.1109/ICGCE.2013.6823499.
• 31. Qin L., Wei D., Sun Y.B. Design of onboard device to diagnose track fault Online. AMM 2013; 483: 465–470. doi: 10.4028/www.scientific.net/AMM.483.465.
• 32. Tsunashima H., Naganuma Y., Kobayashi T. Track geometry estimation from car-body vibration. Vehicle System Dynamics 2014; 52(1): 207–219. doi: 10.1080/00423114.2014.889836.
• 33. Reiterer A. Railway Measurement Techniques: Opportunities and Challenges. In The Second International Conference on Railway Technology: Research, Development and Maintenance, Ajaccio, Corsica, France. 86. doi: 10.4203/ccp.104.86.
• 34. Yeo G.J., Weston P.F., Roberts C. The utility of continual monitoring of track geometry from an inservice vehicle. In 6th IET Conference on Railway Condition Monitoring (RCM 2014), Birmingham, UK: Institution of Engineering and Technology, 2014(2.2.3–2.2.3). doi: 10.1049/cp.2014.0997.
• 35. Roveri N., Carcaterra A., Sestieri A. Remote condition monitoring of railway track using FBG sensors. In Proceedings of the ISMA, 2014.
• 36. Hodge V.J., O’Keefe S., Weeks M., Moulds A. Wireless sensor networks for condition monitoring in the railway industry: A Survey. IEEE Trans. Intell. Transport. Syst 2015; 16(3), 1088–1106, doi: 10.1109/TITS.2014.2366512.
• 37. Weston P., Roberts C., Yeo G., Stewart E. Perspectives on railway track geometry condition monitoring from in-service railway vehicles. Vehicle System Dynamics 2015; 53(7): 1063–1091, doi: 10.1080/00423114.2015.1034730.
• 38. Tsunashima H., Mori H., Ogino M., Azami S., Asano A. Development of track condition monitoring system for conventional railways. Mechanical Engineering 2016; 1115–1124. doi: 10.1201/b21185-119.
• 39. Tsunashima H., Mori H., Ogino M., Azami S., Asano A. The application of an on-board track condition monitoring system for local railways. In The Third International Conference on Railway Technology: Research, Development and Maintenance, Cagliari, Sardinia, Italy, 65. doi: 10.4203/ccp.110.65.
• 40. Ilie G., Stancalie A.A general approach for real-time rail condition monitoring using optical fiber sensors. UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2016; 78, 295–306,
• 41. Maddison S., Smith B. Wireless remote condition monitoring opens new monitoring applications in railway tunnel deformation and trackbed. In Transforming the Future of Infrastructure through Smarter Information: Proceedings of the International Conference on Smart Infrastructure and Construction 2016, 27–29(161–166). doi: 10.1680/tfitsi.61279.161.
• 42. Lienhart W., Wiesmeyr C., Wagner R., Klug F., Litzenberger M., Maicz D. Condition monitoring of railway tracks and vehicles using fibre optic sensing techniques. In Transforming the Future of Infrastructure through Smarter Information - Proceedings of the International Conference on Smart Infrastructure and Construction, ICSIC 2016, ICE Publishing, 2016, 45–50. doi: 10.1680/tfitsi.61279.45.
• 43. [43] Xu X., Zheng J., Yang J., Xu D., Sun X. Track irregularity fault identification Based on Evidence Reasoning rule. In 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Birmingham, United Kingdom: IEEE, 2016; 98–306. doi: 10.1109/ICIRT.2016.7588747.
• 44. Balouchi F., Bevan A., Formston R. Detecting railway under-track voids using multi-train in-service vehicle accelerometer. in 7th iet conference on railway condition Monitoring 2016 (RCM 2016), Birmingham, UK: Institution of Engineering and Technology 2016; 7(6). doi: 10.1049/cp.2016.1194.
• 45. Chellaswamy C., Duraichamy S., Glaretsubin P., Vanathi A. Optimized vehicle acceleration measurement for rail track condition monitoring. In 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India: IEEE, 2017, 155–160. doi: 10.1109/ICCCT2.2017.7972265.
• 46. Seraj F., Meratnia N., Havinga P.J.M. RoVi: Continuous transport infrastructure monitoring framework for preventive maintenance. In 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom), Kona, Big Island, HI, USA: IEEE, 2017, 217–226. doi: 10.1109/PERCOM.2017.7917868.
• 47. Groos J.C., Havrila P., Schubert L.A. In-service railway track condition monitoring by analysis of axle box accelerations for small to mid-size infrastructure operators. In 1st World Congress on Condition Monitoring,
• WCCM 2017. [Online] https://elib.dlr.de/123161/48. Chudzikiewicz A., Bogacz R., Kostrzewski M., Konowrocki R. Condition monitoring of railway track systems by using acceleration signals on wheelset axle-boxes. Transport 2017; 33(2): 555–566, doi: 10.3846/16484142.2017.1342101.
• 49. Zhang Y., Liu, F., Jing Y., Li W. Application of FBG sensing technique for monitoring and early warning system of high-speed railway track conditions. In 25th International Conference on Optical Fiber Sensors, Y. Chung, W. Jin, B. Lee, J. Canning, K. Nakamura, i L. Yuan, Red., Jeju, , Republic of Korea 2017, 103237X. doi: 10.1117/12.2265412.
• 50. Ngamkhanong C., Kaewunruen S., Costa B. State-of-the-Art Review of Railway Track Resilience Monitoring Infrastructures2018; 3(1): 3 doi: 10.3390/infrastructures3010003.
• 51. Känsälä K., Rantala S., Kauppila O., Leviäkangas P. Acceleration sensor technology for rail track asset condition monitoring. Proceedings of the Institution of Civil Engineers - Management, Procurement and Law 2018, 171(1): 32–40, doi:10.1680/jmapl.17.00040.
• 52. Milne D., Pen L.L., Thompson D., Powrie W. Automated processing of railway track deflection signals obtained from velocity and acceleration measurements. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2018; 232(8): 2097–2110. doi:10.1177/0954409718762172.
• 53. Tešić P., Jovanović S., Dick M. Analysis of vehicle/track interaction measurement data using the V/TI Monitor system. JCE 2018; 70(02): 105–119. doi:10.14256/JCE.2067.2017.
• 54. Roth M., Baasch B., Havrila P., Groos J. Mapsupported positioning enables in-service condition monitoring of railway tracks. In 21st International Conference on Information Fusion (FUSION), Cambridge 2018, United Kingdom: IEEE, 2346–2353. doi:10.23919/ICIF.2018.8455377.
• 55. Tam H., Lee K., Liu S., Cho L., Cheng K. Intelligent Optical Fibre Sensing Networks Facilitate Shift to Predictive Maintenance in Railway Systems. In International Conference on Intelligent Rail Transportation (ICIRT), Singapore 2018: IEEE, 1–4. doi: 10.1109/ICIRT.2018.8641602.
• 56. Lu J., Gao M., Wang Y., Wang P. Health monitoring of urban rail corrugation by wireless rechargeable sensor nodes. Structural Health Monitoring 2019; 18(3): 838–852. doi: 10.1177/1475921718782395.
• 57. Chandran P., Rantatalo M., Odelius J., Lind H., Famurewa S.M. Train-based differential eddy current sensor system for rail fastener detection. Meas. Sci. Technol 2019; 30(12): 125105, doi: 10.1088/1361-6501/ab2b24.
• 58. Farkas A. Measurement of railway track geometry: a state-of-the-art review. Period. Polytech. Transp. Eng 2019; 48(1): 76–88. doi:10.3311/PPtr.14145.
• 59. Bhardwaj B., Bridgelall R., Chia L., Lu P., Dhingra N. Signal filter cut-off frequency determination to enhance the accuracy of rail track irregularity detection and localization. IEEE Sensors J 2020; 20(3): 1393–1399, doi:10.1109/JSEN.2019.2947656.
• 60. Aung H.T. Condition monitoring of yangon circular railway and yangon–mandalay railway based on car-body acceleration response using a portable device. JDR 2020; 15(3), 353–359, doi: 10.20965/jdr.2020.p0353.
• 61. Liu X., Markine V.L. Train hunting related fast degradation of a railway crossing—condition monitoring and numerical verification. Sensors 2020; 20(8):2278. doi: 10.3390/s20082278.
• 62. Wilk A. Digital filtering of railway track coordinates in mobile multi–receiver gnss measurements. Sensors 2020; 20(18): 5018. doi:10.3390/s20185018.
• 63. Shah A.A., Bhatti N.A., Dev K., Chowdhry B.S. MUHAFIZ: IoT-Based track recording vehicle for the damage analysis of the railway track. IEEE Internet Things J 2021; 8(11), 9397–9406, doi:10.1109/JIOT.2021.3057835.
• 64. Kukulski J., Gołębiowski P., Makowski J., JacynaGołda I., Żak J. Effective method for diagnosing continuous welded track condition based on experimental research. Energies 2021; 14(10): 2889. doi: 10.3390/en14102889.
• 65. Zvolenský P., Leštinský L., Ďungel J., Grenčík J. Acoustic diagnostics of railway vehicles. Transportation Research Procedia 2021; 55: 667–672, doi:0.1016/j.trpro.2021.07.033.
• 66. Balci E., Yalçin E., Yelce T.U., Bezgı̇N N. Smart diagnosis and maintenance systems for railway tracks. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi 2021; 4(2), 134–147, doi: 10.51513/jitsa.951322.
• 67. Daniyan I., Mpofu K., Nwankwo S. Design of a robot for inspection and diagnostic operations of rail track facilities. IJQRM 2023; 40(3), 653–673. doi:10.1108/IJQRM-03-2020-0083.
• 68. Bolshakova A.V., Boronakhin A.M., Klionsky D.M., Yu Larionov D., Tkachenko A.N., Shalymov R.V. Railway Track Diagnostics by Combined Kinematic and Vibroacoustic Analysis. In International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), Saint Petersburg 2022, Russian Federation: IEEE. 188–192. doi: 10.1109/ITQMIS56172.2022.9976711.
• 69. Tsunashima H., Ono H., Takata T., Ogata S. Development and operation of track condition monitoring system using in-service train. Applied Sciences 2023; 13(6): 3835. doi: 10.3390/app13063835.
• 70. Zanelli F., La Paglia I., Debattisti N., Mauri M., Tarsitano D., Sabbioni E.„Monitoring Railway Infrastructure Through a Freight Wagon Equipped with Smart Sensors. In Experimental Vibration Analysis for Civil Engineering Structures, 432, M.P. Limongelli, P.F. Giordano, S. Quqa, C. Gentile, i A. Cigada, Red., In Lecture Notes in Civil Engineering, 432. Cham: Springer Nature Switzerland, 2023, 193–202. doi: 10.1007/978-3-031-39109-5_20.
• 71. Pal A., Datta A.K. Development of smart real-time fault detection approach in railway track deploying a single acoustic emission sensor data. J. Vib. Eng. Technol., 2024, doi: 10.1007/s42417-024-01374-4.
• 72. Partington W. Wheel impact load monitoring. Proceedings of the Institution of Civil Engineers - Transport 1993; 100(4): 243–245. doi:10.1680/itran.1993.25451. 73. Samuels J.M., Palesano J.J. Using Mainline Wheel Impact Detectors to Increase the Productivity of Car Inspection Functions. Conference on Railway Engineering 1987: Preprints of Papers. Barton, ACT. [Online]. https://search.informit.org/doi/10.3316/informit.465991385409489 https://search.informit.org/doi/pdf/10.3316/informit.465991385409489
• 74. Lechowicz S., Hunt C. Monitoring and Managing Wheel Condition and Loading. 1999. [Online]. Dostępne na: https://api.semanticscholar.org/CorpusID:107721014
• 75. Jergéus J., Odenmarck C., Lundén R., Sotkovszki P., Karlsson B., Gullers P. Full-scale railway wheel flat experiments. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 1999; 213(1): 1–13. doi:10.1243/0954409991530985.
• 76. Danneskiold-Samsøe U., Ramkow-Pedersen O. New on-line monitoring system of surface defects and profile wear. 2001; 125: 429–433,
• 77. Johansson A.N., Nielsen J.C.O. 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 2003; 217, 135–146,
• 78. Thakkar N., Steel J., Reuben R. Rail-wheel contact stress assessment using acoustic emission: A laboratory study of the effects of wheel flats. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2012; 226, 3–13. doi:10.1177/0954409711402138.
• 79. Wei C., Xin Q., Chung W.H., Liu S., Tam H., Ho S.L. Real-time train wheel condition monitoring by fiber bragg grating sensors. International Journal of Distributed Sensor Networks 2011, 8(1): 409048. doi: 10.1155/2012/409048.
• 80. Asplund M., Palo M., Famurewa S., Rantatalo M. A study of railway wheel profile parameters used as indicators of an increased risk of wheel defects. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2016, 230(2): 323–334, doi: 10.1177/0954409714541953.
• 81. Papaelias M., Amini A., Huang Z., Vallely P., Dias D.C., Kerkyras S. 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 2014; 230(3): 709–723. doi: 10.1177/0954409714559758.
• 82. Asplund M., Famurewa S.M., Rantatalo M. Data Quality Assessment of Automatic Wheel Profile Measurement Systems. In Current Trends in Reliability, Availability, Maintainability and Safety, U. Kumar, A. Ahmadi, A K. Verma, P. Varde, Red., Cham: Springer International Publishing, 2016, 717–738.
• 83. Alemi A., Corman F., Lodewijks G. Monitoring of railway wheel diameters by means of a Wheel Impact Load Detector. In Proceedings of the 3rd International Conference on Railway Technology, J. Pombo, Red., w Civil-Comp Proceedings. United Kingdom: Civil-Comp Press, 2016. doi: 10.4203/ccp.110.237.
• 84. Song Y., Sun B. Recognition of wheel polygon based on w/r force measurement by piezoelectric sensors in GSM-R network. Wireless Personal Communications 2018, 102(2): 1283–1291. doi:10.1007/s11277-017-5194-z.
• 85. Xu P. Condition monitoring of wheel wear for highspeed trains: A data-driven approach. In 2018 IEEE International Conference on Prognostics and Health Management (ICPHM) 2018, 1–8. doi: 10.1109/ICPHM.2018.844886486. Ni Y.Q., Zhang Q.H.A. Bayesian Machine Learning Approach for Online Wheel Condition Detection Using Track-side Monitoring. In 2018 International Conference on Intelligent Rail Transportation (ICIRT), 2018, 1–6. doi:10.1109/ICIRT.2018.8641663.
• 87. Gao R., He Q., Feng Q., Cui J. In-service detection and quantification of railway wheel flat by the reflective optical position sensor. Sensors 2020; 20(17). doi:10.3390/s20174969.
• 88. Mosleh A., Montenegro P.A., Costa P.A., Calçada R. Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis. Applied Sciences 2021; 11(9): doi: 10.3390/app11094002.
• 89. Chung K.J., Lin C.W. Condition monitoring for fault diagnosis of railway wheels using recurrence plots and convolutional neural networks (RP-CNN) models. Measurement and Control 2024; 57(3): 330–338. doi: 10.1177/00202940231201376.
• 90. Seliger G., Kross U., Buchholz A. Efficient maintenance approach by on-board monitoring of innovative freight wagon bogie. In Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003), Kobe, Japan: IEEE, 2003, 407–411. doi: 10.1109/AIM.2003.1225130.
• 91. Goda K., Goodall R. Fault-detection-and-isolation system for a railway vehicle bogie. In The Dynamics of Vehicles on Roads and on Tracks Supplement to Vehicle System Dynamics, M. Abe, Red., w Vehicle System Dynamics, vol. 41. United States: CRC Press, 2005, s. 468–476. [Online]. https://www.tandfonline.com/doi/abs/10.1076/vesd.38.5.391.8281
• 92. Li P., Goodall R., Weston P., Seng Ling C., Goodman C., Roberts C. Estimation of railway vehicle suspension parameters for condition monitoring. Control Engineering Practice 2007, 15(1): s. 43–55, doi: https://doi.org/10.1016/j.conengprac.2006.02.021.
• 93. Mei T.X., Ding X.J. Condition monitoring of rail vehicle suspensions based on changes in system dynamic interactions. Vehicle System Dynamics 2009, 47(9): 1167–1181, doi: 10.1080/00423110802553087.
• 94. Ward C.P., Goodall R.M., Dixon R., Charles G. Condition monitoring of rail vehicle bogies. In UKACC International Conference on Control 2010, 1–6. doi:10.1049/ic.2010.0447.
• 95. Tsunashima H., Mori H. Condition monitoring of railway vehicle suspension using adaptive multiple model approach. In ICCAS 2010, 584–589. doi: 10.1109/ICCAS.2010.5669915.
• 96. Daadbin A., Rosinski J., Smurthwaite D. Online monitoring of essential components helps urban transport management and increases the safety of rail transport. Urban and Maritime Transport 2012; 541–552. doi:10.2495/UT120461.
• 97. Amini A., Entezami M., Huang Z., Rowshandel H., Papaelias M. Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high frequency acoustic emission signals. Advances in Mechanical Engineering 2016; 8. doi: 10.1177/1687814016676000.
• 98. De Los Santos N., Jones R., Tarawneh C.M., Fuentes A., Villarreal A. Development of prognostic techniques for surface defect growth in railroad bearing rolling elements. Mechanical Engineering Faculty Publications and Presentations 2017; 4. doi: 10.1115/JRC2017-2262.
• 99. Li C., Luo S., Cole C., Spiryagin M. Bolster spring fault detection strategy for heavy haul wagons. Vehicle System Dynamics 2018; 56(10): 1604–1621. doi: 10.1080/00423114.2017.1423090.
• 100. Chudzikiewicz A., Stelmach A., Wawrzyński W. Selection of signal registration points in the monitoring process and diagnosing the condition of the rail vehicle. Journal of Civil Engineering and Transport 2019; 1, 7–13, doi:10.24136/tren.2019.001.
• 101. Tarawneh C., Lima J.D., Santos N.D.L., Jones R.E. Prognostics Models for Railroad Tapered Roller Bearings with Spall Defects on Inner or Outer Rings. Tribology Transactions 2019; 62(5): 897–906. doi: 10.1080/10402004.2019.1634228.
• 102. Sánchez R.V. Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles. Applied Sciences 2020; 10(12). doi: 10.3390/app10124367.
• 103. Zanelli F., Mauri M., Castelli-Dezza F., Sabbioni E., Tarsitano D., Debattisti N. Energy autonomous wireless sensor nodes for freight train braking systems monitoring. Sensors 2022; 22(5). doi:10.3390/s22051876.
• 104. Guo J., Zhang G., Shi H., Zeng J. Small-amplitude bogie hunting identification method for high-speed trains based on machine learning. Vehicle System Dynamics 2024; 62(5): 1253–1267, doi: 10.1080/00423114.2023.2224906.
• 105. Appun P., Daum M. Fault diagnosis on modern three-phase traction vehicles. In International Conference on Main Line Railway Electrification 1989, 207–211.
• 106. Daley S., Newton D.A., Bennett S.M., Patton R.J. Methods for fault diagnosis in rail vehicle traction and braking systems. In IEE Colloquium on Qualitative and Quantitative Modelling Methods for Fault Diagnosis, 1995, 5: 1–513. doi: 10.1049/ic:19950513.
• 107. Winterling M.W., Tuinman E., Deleroi W. Fault analysis of electromechanical traction drives. In Eighth International Conference on Electrical Machines and Drives (Conf. Publ. No. 444), 1997, 248–252. doi:10.1049/cp:19971077.
• 108. Deuszkiewicz P., Radkowski S. On-line condition monitoring of a power transmission unit of a rail vehicle. Mechanical Systems and Signal Processing 2003; 17(6), 1321–1334, doi: https://doi.org/10.1006/mssp.2002.1578.
• 109. Hedayati Kia S., Henao H., Capolino G.-A. Mechanical health assessment of a railway traction system. In MELECON 2008. The 14th IEEE Mediterranean Electrotechnical Conference, Ajaccio, France: IEEE, 2008, 453–458. doi: 10.1109/MELCON.2008.4618477.
• 110. Sh. H., Kia, H., Henao, G.A. Capolino. Non-Stationary Condition Torsional Vibration Monitoring Using Induction Machine Electromagnetic Torque Estimation. In 2008 18th International Conference on Electrical Machines, Vilamoura: IEEE, 2008, 1–7. doi: 10.1109/ICELMACH.2008.4800056.