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Rail vehicle axle-box bearing damage detection considering the intensity of heating alteration
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Vilnius Gediminas Technical University, Department of Mobile Machinery and Railway Transport, Plytinės Str. 27, 10105 Vilnius, Lithuania
Publication date: 2020-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(4):724–729
  • Axle-box temperature change intensity as criterion of the technical state was provided.
  • Comparative analysis of assessment methods of axle-box temperature change was performed.
  • Three cases to assess the intensity of axle-box temperature change were examined.
  • The applying of Sharp criterion method as the most appropriate of the three is proved.
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.
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,
An automated complex for vibration diagnostics of bearings of axlebox units of wheelsets of railway cars. JSC "Technocom". Watched: 2020-03-05:
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,
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,
Bently D E. Rolling element bearing defect detection and diagnostics using REBAM® probes. Orbit 2001; 22:12-25.
Bosso N. A modular monitoring system for on-board vehicle diagnostic. Mater Eval 2012: 78-85.
Cao P, Fan F, Yang X. Wheel-bearing fault diagnosis trains using empirical wavelet transform. Measurement 2016; 82: 439-449,
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,
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,
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,
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,
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,
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,
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,
Owen R. No bearing no acoustics? Think again. Acoustics Bulletin. Institute of Acoustics 2014; 39 (4): 35-37.
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,
Papaelias M. Interoperable monitoring, diagnosis and maintenance strategies for axle bearings. Maxbe report 2012; 34 p.
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,
Symonds N, Corni I, Wood R. Observing early stage rail axle bearing damage. Eng Fail Anal 2015; 56: 216-232,
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,
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,
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,
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,
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,
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,
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,
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,
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,
Trackside acoustic detection of axle bearing fault using wavelet domain moving beamforming method
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