Identification of Multiple Defects from Rail Vibration Signals Based on Fast Kurtogram
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 6
Abstract
Defects present in the wheel-rail system will seriously affect ride comfort and endanger driving safety. This study aims to identify slight changes in the vibrations caused by the geometric defects of the wheel-track system from the time-frequency characteristics of rail vibration signals, in order to analyze the wheel-track dynamic system for the purpose of defect testing. The fast kurtogram (FK) method is introduced to describe the vibration characteristics in different frequency bands caused by wheel-rail interaction, and capture abnormal vibrations from the characteristics of periodic pulse vibrations. To verify the effectiveness of the proposed method, in situ measured data were analyzed and the similarities and differences of vibration signals of the wheel-rail system when wheels passed over different track structural defects were compared. A coupled vehicle-track dynamic model of a train formation was established, and the abnormal vibration behavior of different wheelsets with polygonal wear when passing over a joint was simulated. It was found that this method can locate a faulty wheel with mixed defects. After processing the field data, the abnormal vibration signals were identified as coming from back-of-flange contact, demonstrating that the proposed method was capable of identifying abnormal wheel-rail dynamic responses. It was found that the FK method can be used to reveal the similarities and differences of vibration responses of different track defects, and can be used to evaluate the transient vibration of wheel-rail contact to locate faulty wheels with mixed defects.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Amini, A., M. Entezami, Z. Huang, H. Rowshandel, and M. Papaelias. 2016. “Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals.” Adv. Mech. Eng. 8 (11): 168781401667600. https://doi.org/10.1177/1687814016676000.
Antoni, J. 2006. “The spectral kurtosis: A useful tool for characterising non-stationary signals.” Mech. Syst. Signal Process. 20 (2): 282–307. https://doi.org/10.1016/j.ymssp.2004.09.001.
Antoni, J., and R. B. Randall. 2006. “The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines.” Mech. Syst. Signal Process. 20 (2): 308–331. https://doi.org/10.1016/j.ymssp.2004.09.002.
Belotti, V., F. Crenna, R. C. Michelini, and G. B. Rossi. 2006. “Wheel-flat diagnostic tool via wavelet transform.” Mech. Syst. Signal Process. 20 (8): 1953–1966. https://doi.org/10.1016/j.ymssp.2005.12.012.
Czop, P., K. Mendrok, and T. Uhl. 2011. “Application of inverse linear parametric models in the identification of rail track irregularities.” Arch. Appl. Mech. 81 (11): 1541–1554. https://doi.org/10.1007/s00419-010-0500-1.
Do, N. T., and M. Gul. 2021. “Estimations of vertical rail bending moments from numerical track deflection measurements using wavelet analysis and radial basis function neural networks.” J. Transp. Eng. Part A Syst. 147 (2): 04020154. https://doi.org/10.1061/JTEPBS.0000489.
Hayashi, Y., H. Tsunashima, Y. Marumo, and IEEE. 2007. “Fault detection of railway vehicle suspensions using multiple model approach.” In Proc., Annual Conf. of the Society of Instrument and Control Engineers. New York: IEEE.
Jin, H., Q. R. Tian, Z. Li, and Z. H. Wang. 2022. “Ability of vibration control using rubberized concrete for tunnel invert-filling.” Constr. Build. Mater. 317 (Jan): 125932. https://doi.org/10.1016/j.conbuildmat.2021.125932.
Johansson, A., and J. C. O. Nielsen. 2003. “Out-of-round railway wheels—Wheel-rail contact forces and track response derived from field tests and numerical simulations.” Proc. Inst. Mech. Eng., Part F: J. Rail Rapid Transit 217 (2): 135–146. https://doi.org/10.1243/095440903765762878.
Lei, Z. Y., and Z. Q. Wang. 2020. “Generation mechanism and development characteristics of rail corrugation of Cologne egg fastener track in metro.” KSCE J. Civ. Eng. 24 (6): 1763–1774. https://doi.org/10.1007/s12205-020-1614-9.
Li, L., M. Li, and F. F. Chen. 2016a. “A fast kurtogram demodulation method in rolling bearing fault diagnosis.” In Proc., 3rd Int. Conf. on Mechanics and Mechatronics Research (ICMMR). Les Ulis, France: EDP Science.
Li, Y. F., J. X. Liu, and Y. Wang. 2016b. “Railway wheel flat detection based on improved empirical mode decomposition.” Shock Vib. 2016 (Jan): 4879283. https://doi.org/10.1155/2016/4879283.
Liang, B., S. D. Iwnicki, Y. Zhao, and D. Crosbee. 2013. “Railway wheel-flat and rail surface defect modelling and analysis by time-frequency techniques.” Veh. Syst. Dyn. 51 (9): 1403–1421. https://doi.org/10.1080/00423114.2013.804192.
Mosleh, A., P. Montenegro, P. A. Costa, and R. Calcada. 2021. “An approach for wheel flat detection of railway train wheels using envelope spectrum analysis.” Struct. Infrastruct. Eng. 17 (12): 1710–1729. https://doi.org/10.1080/15732479.2020.1832536.
Rouillard, V., and R. Richmond. 2007. “A novel approach to analysing and simulating railcar shock and vibrations.” Packag. Technol. Sci. 20 (1): 17–26. https://doi.org/10.1002/pts.739.
Sadeghi, J., Y. Rahimizadeh, A. Khajehdezfuly, M. Rezaee, and E. R. Najafabadi. 2020. “Development of rail-condition assessment model using ultrasonic technique.” J. Transp. Eng. Part A Syst. 146 (8): 04020078. https://doi.org/10.1061/JTEPBS.0000390.
Salvador, P., V. Naranjo, R. Insa, and P. Teixeira. 2016. “Axlebox accelerations: Their acquisition and time-frequency characterisation for railway track monitoring purposes.” Measurement 82 (Mar): 301–312. https://doi.org/10.1016/j.measurement.2016.01.012.
Wan, S. T., X. Zhang, and L. J. Dou. 2018. “Compound fault diagnosis of bearings using improved fast spectral kurtosis with VMD.” J. Mech. Sci. Technol. 32 (11): 5189–5199. https://doi.org/10.1007/s12206-018-1017-8.
Wang, L., Z. W. Liu, Q. Miao, and X. Zhang. 2018. “Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis.” Mech. Syst. Signal Process. 103 (Mar): 60–75. https://doi.org/10.1016/j.ymssp.2017.09.042.
Wei, C. L., Q. Xin, W. H. Chung, S. Y. Liu, H. Y. Tam, and S. L. Ho. 2012. “Real-Time Train wheel condition monitoring by fiber Bragg grating sensors.” Int. J. Distrib. Sens. Netw. 8 (1): 409048. https://doi.org/10.1155/2012/409048.
Wu, T. X., and D. J. Thompson. 2004. “The effects of track non-linearity on wheel/rail impact.” Proc. Inst. Mech. Eng., Part F: J. Rail Rapid Transit 218 (1): 1–15. https://doi.org/10.1243/095440904322804394.
Xiao, X., Z. Sun, and W. A. Shen. 2020. “A Kalman filter algorithm for identifying track irregularities of railway bridges using vehicle dynamic responses.” Mech. Syst. Signal Process. 138 (Apr): 106582. https://doi.org/10.1016/j.ymssp.2019.106582.
Zheng, X., X. Tao, and F. Wang. 2010. “Virtual inversion of vibration source parameters of wheel-rail impact.” J. Harbin Inst. Technol. 42 (8): 1232–1236.
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© 2023 American Society of Civil Engineers.
History
Received: Nov 13, 2022
Accepted: Jan 30, 2023
Published online: Apr 12, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 12, 2023
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