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Rail vehicle axle-box bearing damage detection considering the intensity of heating alteration

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article observes problems of detection of rolling bearing damages in rail vehicles. Two methods of bearing damage detection are examined – according to heating of axleboxes and by vibro-diagnostic manner. The disadvantage of vibro-diagnostic method is that a contact vibration sensor is used for vibration diagnostics, intervention into rail vehicle structure is required. The method according to heating of axle-boxes also has drawbacks. The same temperature value of axle-box in various conditions may characterize different bearing technical state. The Authors studied a possibility to use temperature change intensity parameters as the diagnostics criteria. Based on the examples of axle-box temperature measurement data, Authors developed and proposed a methodology for detecting axle-box bearings defects. The Authors suggest the use the method according to heating of axle-boxes. The given example proofs that the fact of the presence of their damages can be unambiguously identified by the intensity of temperature change of the axle-boxes.
Rocznik
Strony
724--729
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Vilnius Gediminas Technical University, Department of Mobile Machinery and Railway Transport, Plytinės Str. 27, 10105 Vilnius, Lithuania
  • Vilnius Gediminas Technical University, Department of Mobile Machinery and Railway Transport, Plytinės Str. 27, 10105 Vilnius, Lithuania
  • Vilnius Gediminas Technical University, Department of Mobile Machinery and Railway Transport, Plytinės Str. 27, 10105 Vilnius, Lithuania
Bibliografia
  • 1. Amini A, Entezami M, Papaelias M. Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals. J Case Stud Nondestruct Test Eval 2016; 6: 8-16, https://doi.org/10.1016/j.csndt.2016.06.002.
  • 2. An automated complex for vibration diagnostics of bearings of axlebox units of wheelsets of railway cars. JSC "Technocom". Watched: 2020-03-05: http://texnokom-nn.ru/katalog/kolesno-rolikovyi-uchastok/komplex-vibrodiagnostiki-sv-tk/?utm_source=google&utm_medium=text&gclid=EAIaIQobChMI2ou1__6C6AIVyPhRCh30TAtWEAAYASAAEgJvXvD_BwE
  • 3. Aliev T, Babayev T, Alizada T, Rzayeva N. Control Of The Beginning Of Accidents In Railroad Operation Safety Systems In Seismically Active Regions Using The Noise Technology. Transport Problems - Problemy Transportu 2019; 14 (3): 155 - 162, https://doi.org/10.20858/tp.2019.14.3.14
  • 4. Aliev T, Babayev T, Alizada T, Rzayeva N. Noise control of the beginning and development dynamics of faults in the running gear of the rolling stock. Transport Problems - Problemy Transportu 2020; 15 (2): 83 - 91, https://doi.org/10.21307/tp-2020-022.
  • 5. Bently D E. Rolling element bearing defect detection and diagnostics using REBAM® probes. Orbit 2001; 22:12-25.
  • 6. Bosso N. A modular monitoring system for on-board vehicle diagnostic. Mater Eval 2012: 78-85.
  • 7. Cao P, Fan F, Yang X. Wheel-bearing fault diagnosis trains using empirical wavelet transform. Measurement 2016; 82: 439-449, https://doi.org/10.1016/j.measurement.2016.01.023.
  • 8. Gomez M J, Castejon C, Garcia-Prada J C. New stopping criteria for crack detection during fatigue tests of railway axles. Engineering Failure Analysis 2015; 56: 530-537, https://doi.org/10.1016/j.engfailanal.2014.10.018.
  • 9. Han T, Jiang D. Fault diagnosis of multistage centrifugal pump unit using non-local means-based vibration signal denoising. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (4): 539-545, https://doi.org/10.17531/ein.2019.4.1.
  • 10. Huang Y, Lin J, Liu Z, Wu W. A modified scale-space guiding vibrational mode decomposition for high-speed railway bearing fault diagnosis. Journal of Sound and Vibration 2019; 444: 216-234, https://doi.org/10.1016/j.jsv.2018.12.033.
  • 11. Huang H-Z, Yu K, Huang T, Li H, Qian H-M. Reliability estimation for momentum wheel bearings considering frictional heat. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (1): 6-14, https://doi.org/10.17531/ein.2020.1.2.
  • 12. Karabacak Y., Gürsel Özmen N, Gümüşel L. Worm gear condition monitoring and fault detection from thermal images via deep learning method. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (3): 544-556, https://doi.org/10.17531/ein.2020.3.18.
  • 13. Li X, Jis L., Yang X. Fault diagnosis of train axle bearing based on multiply feature parameters. Dissert Dynamics in Nature and Society 2015. Article ID 846918: 8, https://doi.org/10.1155/2015/846918.
  • 14. Li Y, Liang X, Li J. Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter. Mech Syst Signal Pr 2018; 101: 435-448, https://doi.org/10.1016/j.ymssp.2017.09.007.
  • 15. Owen R. No bearing no acoustics? Think again. Acoustics Bulletin. Institute of Acoustics 2014; 39 (4): 35-37.
  • 16. Papaelias M, Amini A, Huang Z. Online conduction monitoring of rolling stock wheels and axle bearing. J. Rail Rapid Transit 2016; 230 (3): 709-723, https://doi.org/10.1177/0954409714559758.
  • 17. Papaelias M. Interoperable monitoring, diagnosis and maintenance strategies for axle bearings. Maxbe report 2012; 34 p.
  • 18. Steišūnas S, Bureika G, Gorbunov M. Effects of rail-wheel parameters on vertical vibrations of vehicles using a vehicle-track-coupled model. Transport Problems - Problemy Transportu 2019; 14 (3); 27-39, https://doi.org/10.20858/tp.2019.14.3.3.
  • 19. Symonds N, Corni I, Wood R. Observing early stage rail axle bearing damage. Eng Fail Anal 2015; 56: 216-232, https://doi.org/10.1016/j.engfailanal.2015.02.008.
  • 20. Urbaś A, Szczotka M. The influence of the friction phenomenon on a forest crane operator's level of discomfort. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (2): 197-210, https://doi.org/10.17531/ein.2019.2.3.
  • 21. Vaičiūnas G, Bureika G, Steišūnas S. Research on metal fatigue of rail vehicle wheel considering the wear intensity of rolling surface. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20 (1): 24-29, https://doi.org/10.17531/ein.2018.1.4.
  • 22. Vale C, Bonifacio C, Seabra J. Novel efficient technologies in Europe for axle bearing condition monitoring - the MAXBE project. Transport. Res. Proc. 2016; 14: 635-644, https://doi.org/10.1016/j.trpro.2016.05.313.
  • 23. Wang Z, Cheng Y, Allen P, Zhonghui Yin Z, Zou D, Zhang W. Analysis of vibration and temperature on the axle box bearing of a high-speed train. Vehicle System Dynamics, International Journal of Vehicle Mechanics and Mobility 2019, https://doi.org/10.1080/00423114.2019.1645340.
  • 24. Wang C, Shen C, He Q. Wayside acoustic defective bearing detection based on improved Doppler-let transform and Doppler transient matching. Appl. Acoust. 2016; 101: 141-155, https://doi.org/10.1016/j.apacoust.2015.08.014.
  • 25. Yi C, Lin J, Zhang W, Ding J. Faults diagnostics of railway axle bearings based on IMF's confidence index algorithm for ensemble EMD. Sensors 2015; 15: 10991-11011, https://doi.org/10.3390/s150510991.
  • 26. Yi C, Wang D, Fan W, Tsui K-L, Lin J. EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings. Sensors 2018; 18(3), 704: 1-21, https://doi.org/10.3390/s18030704.
  • 27. Zhao M, Lin J, Miao Y. Detection on recovery of fault impulses proved harmonic product and its application in defect size estimation of train bearings. Measurement 2016; 91: 421-439, https://doi.org/10.1016/j.measurement.2016.05.068.
  • 28. Zhou Y, Lin L, Wang D, He M, He D. A new method to classify railway vehicle axle fatigue crack AE signal. Applied Acoustics 2018; 131: 174-185, https://doi.org/10.1016/j.apacoust.2017.10.025.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-11f6ba76-4a55-4c58-b1fb-ba3c96fd8bcf
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