Smart Structures and Systems

  • 제21권5호
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  • Pages.705-713
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  • 2018
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Online condition assessment of high-speed trains based on Bayesian forecasting approach and time series analysis

  • Zhang, Lin-Hao (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Wang, You-Wu (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Ni, Yi-Qing (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Lai, Siu-Kai (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • 투고 : 2017.12.20
  • 심사 : 2018.03.31
  • 발행 : 2018.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

키워드

과제정보

연구 과제 주관 기관 : Research Grants Council of the Hong Kong, Ministry of Science and Technology of China, National Rail Transit Electrification and Automation Engineering Technology Research Center, National Natural Science Foundation of China

참고문헌

  1. Alexandrou, G., Kouroussis, G. and Verlinden, O. (2016), "A comprehensive prediction model for vehicle/track/soil dynamic response due to wheel flats", Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit, 230(4), 1088-1104. https://doi.org/10.1177/0954409715576015
  2. Barke, D.W. and Chiu, W. K. (2005a), "A review of the effects of out-of-round wheels on track and vehicle components", Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit, 219(3), 151-175. https://doi.org/10.1243/095440905X8853
  3. Barke, D.W. and Chiu, W.K. (2005b), "Structural health monitoring in the railway industry: A review", Struct. Health Monit., 4(1), 81-93. https://doi.org/10.1177/1475921705049764
  4. Bian, J., Gu, Y. and Murray, M. (2013), "Numerical study of impact forces on railway sleepers under wheel flat", Adv. Struct. Eng., 16(1), 127-134. https://doi.org/10.1260/1369-4332.16.1.127
  5. Bornn, L., Farrar, C.R., Park, G. and Farinholt, K. (2009), "Structural health monitoring with autoregressive support vector machines", J. Vib. Acoust., 131(2), 021004. https://doi.org/10.1115/1.3025827
  6. Brownjohn, J.M. (2007), "Structural health monitoring of civil infrastructure", Philos. Trans. A Math. Phys. Eng. Sci., 365(1851), 589-622. https://doi.org/10.1098/rsta.2006.1925
  7. Catbas, F.N., Susoy, M. and Frangopol, D.M. (2008), "Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data", Eng. Struct., 30(9), 2347-2359. https://doi.org/10.1016/j.engstruct.2008.01.013
  8. China National Bureau of Standards (1985), Railway Vehicles Specification for Evaluation of Dynamic Performance and Accreditation Test, GB5599, Beijing, China.
  9. Dervilis, N., Shi, H., Worden, K. and Cross, E.J. (2016). "Exploring environmental and operational variations in SHM data using heteroscedastic Gaussian processes", Dynamics of Civil Structures, (Eds., S. Pakzad, and C. Juan), Springer, Cham.
  10. Ding, L.Y., Zhou, C., Deng, Q.X., Luo, H.B., Ye, X.W. and Ni, Y. Q., Guo, P. (2013), "Real-time safety early warning system for cross passage construction in Yangtze riverbed metro tunnel based on the internet of things", Automat. Constr., 36, 25-37. https://doi.org/10.1016/j.autcon.2013.08.017
  11. Elangovan, M., Devasenapati, S.B., Sakthivel, N.R. and Ramachandran, K I. (2011), "Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm", Exp. Syst. Appl., 38(4), 4450-4459. https://doi.org/10.1016/j.eswa.2010.09.116
  12. Geem, Z.W., Tseng, C.L., Kim, J. and Bae, C. (2007), "Trenchless water pipe condition assessment using artificial neural network", Pipelines 2007: Advances and Experiences with Trenchless Pipeline Projects, 1-9.
  13. Gharibnezhad, F., Mujica, L.E. and Rodellar, J. (2015), "Applying robust variant of principal component analysis as a damage detector in the presence of outliers", Mech. Syst. Signal Pr., 50-51, 467-479. https://doi.org/10.1016/j.ymssp.2014.05.032
  14. Hu, Q., Lam, H.F. and Alabi, S.A. (2015), "Use of measured vibration of in-situ sleeper for detecting underlying railway ballast damage", Int. J. Struct. Stab. Dyn., 15(8), 1540026. https://doi.org/10.1142/S021945541540026X
  15. Huang, Z.W., Cui, D.B., Du, X. and Jin, X.S. (2013), "Influence of deviated wear of wheel on performance of high-speed train running on straight tracks", J. China Railw. Soc., 35(2), 14-20.
  16. International Union of Railways (1994), Guidelines for Evaluating Passenger Comfort in Relation to Vibration in Railway Vehicles, UIC 513R, Paris, France.
  17. Jeffreys, H. (1961), Theory of Probability, Clarendon Press, Oxford, UK.
  18. Jiang, X. and Mahadevan, S. (2008), "Bayesian wavelet methodology for structural damage detection", Struct. Control Health Monit., 15(7), 974-991. https://doi.org/10.1002/stc.230
  19. Kijewski-Correa, T., Kilpatrick, J., Kareem, A., Kwon, D.K., Bashor, R., Kochly, M., Young, B.S., Abdelrazaq, A., Galsworthy, J., Isyumov, N., Morrish, D., Sinn, R.C. and Baker, W.F. (2006), "Validating wind-induced response of tall buildings: Synopsis of the Chicago full-scale monitoring program", J. Struct. Eng., 132(10), 1509-1523. https://doi.org/10.1061/(ASCE)0733-9445(2006)132:10(1509)
  20. Kim, Y.G., Kwon, H.B., Kim, S.W., Kim, C.K. and Kim, T.W. (2003), "Correlation of ride comfort evaluation methods for railway vehicles", Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit, 217(2), 73-88. https://doi.org/10.1243/095440903765762823
  21. Kim, Y.G., Kim, S.W., Park, C.K., Kim, K.H. and Park, T.W. (2004), "Analysis on the characteristics of the ride comfort for high speed trains on the high-speed line/conventional line", Transactions of the Korean Society for Noise and Vibration Engineering, 14(10), 999-1006. https://doi.org/10.5050/KSNVN.2004.14.10.999
  22. Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  23. Kuok, S.C. and Yuen, K.V. (2012), "Structural health monitoring of Canton Tower using Bayesian framework", Smart Struct. Syst., 10(4), 375-391. https://doi.org/10.12989/sss.2012.10.4_5.375
  24. Laory, I., Trinh, T.N., Posenato, D. and Smith, I.F. (2013), "Combined model-free data-interpretation methodologies for damage detection during continuous monitoring of structures", J. Comput. Civ. Eng., 27(6), 657-666. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000289
  25. Lin, C. C., Wang, C. E., Wu, H. W. and Wang, J. F. (2005), "Online building damage assessment based on earthquake records", Smart Mater. Struct., 14(3), S137-S153. https://doi.org/10.1088/0964-1726/14/3/017
  26. Lipowsky, H., Staudacher, S., Bauer, M. and Schmidt, K.J. (2010), "Application of Bayesian forecasting to change detection and prognosis of gas turbine performance", J. Eng. Gas Turb. Power, 132(3), 031602. https://doi.org/10.1115/1.3159367
  27. Lynch, J.P. (2007), "An overview of wireless structural health monitoring for civil structures", Philos. T. A Math. Phys. Eng. Sci., 365(1851), 345-372. https://doi.org/10.1098/rsta.2006.1932
  28. Mohamad, H., Soga, K., Bennett, P.J., Mair, R.J. and Lim, C.S. (2012), "Monitoring twin tunnel interaction using distributed optical fiber strain measurements", J. Geotech. Geoenviron. Eng., 138(8), 957-957. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000656
  29. Nakagawa, C., Shimamune, R., Watanabe, K. and Suzuki, E. (2010), "Fundamental study on the effect of high frequency vibration on ride comfort", J. Mech. Syst. Transp. Logist., 3(1), 287-293. https://doi.org/10.1299/jmtl.3.287
  30. Ni, Y.Q., Lin, K.C., Wu, L.J. and Wang, Y.W. (2017), "Visualized spatiotemporal data management system for lifecycle health monitoring of large-scale structures", J. Aerosp. Eng., 30(2), B4016007. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000622
  31. Ni, Y.Q., Liu, X.Z., Zhao, W.Z. and Liang, S.L. (2015), "Outlier detection in sensor-assisted online ride comfort assessment of high-speed trains", Proceedings of the 10th International Workshop on Structural Health Monitoring, Stanford, California, USA.
  32. Ni, Y. Q., Wang, B. S. and Ko, J. M. (2002), "Constructing input vectors to neural networks for structural damage identification", Smart Mater. Struct., 11(6), 825-833. https://doi.org/10.1088/0964-1726/11/6/301
  33. Ni, Y.Q., Wong, K.Y. and Xia, Y. (2011), "Health checks through landmark bridges to sky-high structures", Adv. Struct. Eng., 14(1), 103-119. https://doi.org/10.1260/1369-4332.14.1.103
  34. Ni, Y. Q., Xia, Y., Liao, W. Y. and Ko, J. M. (2009), "Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower", Struct. Control Health Monit., 16(1), 73-98. https://doi.org/10.1002/stc.303
  35. Nielsen, J.C. and Johansson, A. (2000). "Out-of-round railway wheels-a literature survey", Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit, 214(2), 79-91. https://doi.org/10.1243/0954409001531351
  36. Niu, Y., Fritzen, C.P., Jung, H., Buethe, I., Ni, Y.Q. and Wang, Y. W. (2015), "Online simultaneous reconstruction of wind load and structural responses - Theory and application to Canton Tower", Comput.-Aided Civ. Infrastruct. Eng., 30(8), 666-681. https://doi.org/10.1111/mice.12134
  37. Ou, J. and Li, H. (2010), "Structural health monitoring in mainland China: review and future trends", Struct. Health Monit., 9(3), 219-231. https://doi.org/10.1177/1475921710365269
  38. Petris, G., Petrone, S. and Campagnoli, P. (2009), Dynamic Linear Models with R, Springer, Berlin, Germany.
  39. Pole, A., West, M. and Harrison, J. (1994), Applied Bayesian Forecasting and Time Series Analysis, CRC Press, Boca Raton, Florida, USA.
  40. Remennikov, A. M. and Kaewunruen, S. (2008), "A review of loading conditions for railway track structures due to train and track vertical interaction", Struct. Control Health Monit., 15(2), 207-234. https://doi.org/10.1002/stc.227
  41. Sohn, H. and Oh, C.K. (2009), "Statistical pattern recognition", Encyclopedia of Structural Health Monitoring, (Eds., C. Boller, F.-K. Chang and Y. Fujino), John Wiley & Sons, Chichester, UK.
  42. Spencer, B.F. Jr., Ruiz-Sandoval, M.E. and Kurata, N. (2004), "Smart sensing technology: opportunities and challenges", Struct. Health Monit., 11(4), 349-368. https://doi.org/10.1002/stc.48
  43. Vernersson, T. (1999), "Thermally induced roughness of treadbraked railway wheels: Part 1: brake rig experiments", Wear, 236(1), 96-105. https://doi.org/10.1016/S0043-1648(99)00260-4
  44. Wan, H.P. and Ni, Y.Q. (2018), "Bayesian modeling approach for forecast of structural stress response using structural health monitoring data", J. Struct. Eng., DOI: 10.1061/(ASCE)ST.1943-541X.0002085.
  45. Wang, J., Liu, X.Z. and Ni, Y.Q. (2018), "A Bayesian probabilistic framework for acoustic emission based rail condition assessment", Comput.-Aided Civ. Infrastruct. Eng., 33(1), 21-34. https://doi.org/10.1111/mice.12316
  46. Wang, X, Ni, Y.Q, Zhang, L.H. and Sun, Q. (2016), "Understanding of dynamic interaction of an in-service highspeed train via on-board monitoring", Presented on the 1st International Workshop on Structural Health Monitoring for Railway System, Qingdao, China.
  47. West, M. and Harrison, J. (1997), Bayesian Forecasting and Dynamic Models, Springer-Verlag, New York, USA.
  48. Wong, K.Y. (2004), "Instrumentation and health monitoring of cable-supported bridges", Struct. Control Health Monit., 11(2), 91-124. https://doi.org/10.1002/stc.33
  49. Worden, K. and Manson, G. (2007), "The application of machine learning to structural health monitoring", Philos. T, A Math. Phys. Eng. Sci., 365(1851), 515-537. https://doi.org/10.1098/rsta.2006.1938
  50. Xia, H.W., Ni, Y.Q., Wong, K.Y. and Ko, J.M. (2012), "Reliability-based condition assessment of in-service bridges using mixture distribution models", Comput. Struct., 106-107, 204-213. https://doi.org/10.1016/j.compstruc.2012.05.003
  51. Yang, B.S., Hwang, W.W., Kim, D.J. and Tan, A.C. (2005), "Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines", Mech. Syst. Signal Pr., 19(2), 371-390. https://doi.org/10.1016/j.ymssp.2004.06.002
  52. Ye, X.W., Ni, Y.Q. and Yin, J.H. (2013), "Safety monitoring of railway tunnel construction using FBG sensing technology", Adv. Struct. Eng., 16(8), 1401-1409. https://doi.org/10.1260/1369-4332.16.8.1401
  53. Yun, C.B., Lee, J.J. and Koo, K.Y. (2011), "Smart structure technologies for civil infrastructures in Korea: recent research and applications", Struct. Infrastruct. E., 7(9), 673-688. https://doi.org/10.1080/15732470902720109
  54. Zhang, F.L., Ni, Y.Q., Ni, Y.C. and Wang, Y.W. (2016), "Operational modal analysis of Canton Tower by a fast frequency domain Bayesian method", Smart Struct. Syst., 17(2), 209-230. https://doi.org/10.12989/sss.2016.17.2.209
  55. Zhou, J., Goodall, R., Ren, L. and Zhang, H. (2009), "Influences of car body vertical flexibility on ride quality of passenger railway vehicles", Proc. Instit. Mech. Eng., Part F: J. Rail Rapid Transit, 223(5), 461-471. https://doi.org/10.1243/09544097JRRT272

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