Railway track inspection based on the vibration response to a scheduled train and the Hilbert

Abstract

With the development of high-density intercity railway networks, substantial investments are now required, in terms of labor and machinery, in order to be able to conduct safety inspections. This results in high operational costs. High-capacity and high-speed operations have resulted in levels of damage and deterioration of railway system components that have surpassed all expected values. Thus, traditional methods of periodic inspection, though still necessary, are no longer sufficient to detect the rapid development of defects on railway systems. Therefore, the direct use of operational trains as inspection vehicles to detect defects in real-time has become a current trend in the development of inspection techniques. This study applies an inspection technique previously reported in the literature to on-site testing of track. The response to vibrations on railway bridges, track system components and track irregularities are also studied. The effects are analyzed using the Hilbert–Huang transform approach. It is shown that the proposed data analysis method can be used in conjunction with the routine operation of trains to create a method for the monitoring of track defects.

Keywords

Vehicle response characteristic axle-box accelerometer track irregularity Hilbert–Huang transform versine

Get full access to this article

View all access options for this article.

References

Ahmadian M. Filtering effects of mid-chord offset measurements on track geometry data. In: The ASME/IEEE joint railroad conference, 1999, pp.157–161.

Grassie

. Measurement of railhead longitudinal profiles: a comparison of different techniques. Wear 1996; 191: 245–251.

Yang

Lin

Yau

. Extracting the bridge frequencies from the dynamic response of a passing vehicle. J. Sound Vib 2003; 272: 471–493.

Yang

Lin

. Vehicle-bridge interaction dynamics and potential applications. J Sound Vib 2005; 284: 205–226.

Lin

Yang

. Use of a passing vehicle to scan the fundamental bridge frequencies: an experimental verification. Engng Struct 2005; 27: 1865–1878.

Takeshita K. A study on the inspection method of track irregularities. Report no. 25, 1998. RITI.

Yazawa

Takeshita

. Development of measurement device of track irregularity using inertial mid-chord offset method. QR RTRI 2002; 43(3): 125–130.

Sunaga Y, Saro I and Ide T. A practical use of axle-box acceleration to control the short wave track irregularities. Report no. 2, 1995. RITI.

Bocciolone

Caprioli

Cigada

Collina

. A measurement system for quick track inspection and effective track maintenance strategy. Mech Syst Signal Process 2007; 21: 1242–1254.

10.

Molodova

Dollevoet

. Axle-box acceleration measurement and simulation for detection of short track defects. Wear 2011; 271: 349–356.

11.

Waston

Ling

Roberts

. Monitoring vertical track irregularity from in-service railway vehicles. Proc IMechE, Part F: J Rail Rapid Transit 2006; 221(1): 75–88.

12.

Waston

Ling

Goodman

. Monitoring lateral track irregularity from in-service railway vehicles. Proc IMechE, Part F: J Rail Rapid Transit 2006; 221(1): 89–100.

13.

Massel

. Power spectrum analysis: modern tool in the study of rail surface corrugations. NDT and E Int 1999; 32: 429–436.

14.

MXD

Berggren

Berg

Persson

. Assessing track geometry quality based on wavelength spectra and track vehicle dynamic interaction. Veh Syst Dyn 2008; 46: 261–276.

15.

Berggren

MXD

Spännar

. A new approach to the analysis and presentation of vertical track geometry quality and track roughness. Wear 2008; 265: 1488–1496.

16.

Hayashi Y, Kojima T, Tsunashima H and Marumo Y. Real time fault detection of railway vehicles and track. In: The fourth IET international conference on railway condition monitoring, 2006, pp.20–25.

17.

Caprioli

Cigada

Raveglia

. Track inspection in track maintenance: a benchmark between the wavelet approach and the more conventional Fourier analysis. Mech Syst Signal Process 2007; 21: 631–652.

18.

Huang

Shen

Long

. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Roy Soc Lond, A 1998; 454: 903–995.

19.

Flandrin

Rilling

Gonçalves

. Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 2004; 11: 112–114.

20.

Tsai

Wang

Huang

. Fast inspection and identification techniques for track irregularities based on HHT analysis. Adv Adap Data Anal 2012; 4(1&2): 1–26.

21.

Huang

Shen

Long

. A new view of nonlinear water waves: the Hilbert spectrum. Ann Rev Fluid Mech 1999; 31: 417–457.

22.

Huang

. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009; 1(1): 1–41.

23.

Huang

Chen

. On Hilbert spectral representation: a true time-frequency representation for nonlinear and nonstationary data. Adv Adapt Data Anal 2011; 3(1 & 2): 63–93.

24.

Thompson

. On the parametric excitation of the wheel/track system. J Sound Vib 2004; 278: 725–7474.

25.

Lin

. Resonance characteristics of high speed trains passing simply supported bridges. J Sound Vib 2003; 267: 1127–1141.

26.

Huang NE, Salvino LW, Nieh YY, et al. HHT-based structural health monitoring. In: Haldar A (ed.) Health assessment of engineered structures: bridge, building and other infrastructures. Singapore: World Scientific, 2013, pp. 203–240.

Railway track inspection based on the vibration response to a scheduled train and the Hilbert–Huang transform

Abstract

Keywords

Get full access to this article

References