The use of sensors fixed to in-service trains has the potential to provide real-time track condition monitoring to inform maintenance planning. An Irish Rail intercity train was instrumented for a period of 1 month so that a numerical method developed to find track longitudinal profile from measured vehicle inertial responses could be experimentally tested. A bogie-mounted accelerometer and gyrometer measured vertical acceleration and angular velocity as the train made regular service operations between Dublin and Belfast on the island of Ireland. Cross entropy optimisation is used to find a track longitudinal profile that generates a numerical inertial response that best fits the measured response. Tolerance limits are used to inject variance where required to ensure a good match between measured and modelled signals. A section of track with known track settlement history is selected as a case study. A level survey was undertaken during the measurement campaign to characterise the longitudinal profile through the test section. Bandpass filters are used to compare inferred profiles and the surveyed profile. Good agreement is found between the two profiles although improvements in accuracy and reproducibility are required before conformance with current standards is achieved.
BrownjohnJMW. Structural health monitoring of civil infrastructure. Philos T R Soc A2007; 365: 589–622.
2.
GoodallRMRobertsC.Concepts and techniques for railway condition monitoring. In: Proceedings of the IET international conference on railway condition monitoring (ed CRoberts), Birmingham, 29–30 November 2006, pp. 90–95. Stevenage: Institution of Engineering and Technology.
3.
European Committee for Standardization (CEN). Railway applications – track – track geometry quality – part 1: characterisation of track geometry (EN13848–1). Brussels: CEN, 2003.
4.
WestonPRobertsCYeoGet al. Perspectives on railway track geometry condition monitoring from in-service railway vehicles. Vehicle Syst Dyn2015; 53: 1063–1091.
5.
WardCPWestonPStewartEet al. Condition monitoring opportunities using vehicle-based sensors. P I Mech Eng F: J Rai2011; 225: 202–218.
6.
SunagaYIDETKanaoM. A practical use of axlebox acceleration to control the short-wave track irregularities on Shinkansen. QR RTRI1995; 9: 35–40.
7.
HayashiYKojimaTTsunashimaHet al. Real time fault detection of railway vehicles and tracks. In: Proceedings of the IET international conference on railway condition monitoring (ed RobertsC), Birmingham, 29–30 November 2006, pp. 20–25. Stevenage: Institution of Engineering and Technology.
8.
MolodovaMLiZDollevoetR.Axle box acceleration: measurement and simulation for detection of short track defects. Wear2011; 271: 349–356.
9.
LiZMolodovaMNúñezAet al. Improvements in axle box acceleration measurements for the detection of light squats in railway infrastructure. IEEE T Ind Electron2014; 62: 4385–4397.
10.
LedermanGChenSGarrettJet al. Track-monitoring from the dynamic response of an operational train. Mech Syst Signal Pr2016; 87: 1–16.
11.
LedermanGChenSGarrettJet al. Track monitoring from the dynamic response of a passing train: a sparse approach. Mech Syst Signal Pr2017; 90: 141–153.
12.
European Committee for Standardization (CEN). Railway applications – line categories for managing the interface between load limits of vehicles and infrastructure (EN15528). Brussels: CEN, 2015.
13.
AhmadianM.Filtering effects of mid-cord offset measurements. In: Proceedings of the 1999 ASME/IEEE joint railroad conference (eds KinghamJWolfE), Dallas, TX, 15 April 1999, pp. 157–161. New York: IEEE.
14.
YazawaETakeshitaK.Development of measurement device of track irregularity using inertial mid-chord offset method. QR RTRI2002; 43: 125–130.
15.
WestonPLingCSRobertsCet al. Monitoring vertical track irregularity from in-service railway vehicles. P I Mech Eng F: J Rai2007; 221: 75–88.
16.
WestonPLingCSGoodmanCet al. Monitoring lateral track irregularity from in-service railway vehicles. P I Mech Eng F: J Rai2007; 221: 89–100.
17.
TsaiHCWangCYHuangNE.Fast inspection and identification techniques for track irregularities based on HHT analysis. Adv Adap Data Anal2012; 4: 1250016.
18.
TsaiHCWangCYHuangNEet al. Railway track inspection based on the vibration response to a scheduled train and the Hilbert-Huang transform. P I Mech Eng F: J Rai2014; 229: 815–829.
19.
OdashimaMAzamiSNaganumaYet al. Track geometry estimation of a conventional railway from car-body acceleration measurement. Mech Eng J2017; 4: 1–12.
20.
BoccioloneMCaprioliACigadaAet al. A measurement system for quick rail inspection and effective track maintenance strategy. Mech Syst Signal Pr2007; 21: 1242–1254.
21.
CaprioliACigadaARavegliaD.Rail inspection in track maintenance: a benchmark between the wavelet approach and the more conventional Fourier analysis. Mech Syst Signal Pr2007; 21: 631–652.
22.
CoudertF.Use of axle box acceleration to detect track and rail irregularities. In: Proceeding of the world congress on railway research, Tokyo, Japan, 19–23 October 1999.
23.
AlfiSBruniS.Estimation of long wavelength track irregularities from on board measurement. In: Proceedings of the 4th IET international conference on railway condition monitoring, Derby, 18–20 June 2008, pp. 85–90. New York: IEEE.
24.
RealJSalvadorPMontalbanLet al. Determination of rail vertical profile through inertial methods. P I Mech Eng F: J Rai2011; 225: 14–23.
25.
LeeJSChoiSKimSSet al. A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie. IEEE T Instrum Meas2012; 61: 749–758.
26.
MoriHTsunashimaHKojimaTet al. Condition monitoring of railway track using in-service vehicle. J Mech Syst Transp Logist2010; 3: 154–165.
27.
IshiiHFujinoYMizunoYet al. The study of train intelligent monitoring system using acceleration of ordinary trains. In: Proceedings of the first Asia-pacific workshop on SHM, Yokohama, 4–6 December 2006.
28.
OBrienEJBoweCQuirkePet al. Determination of longitudinal profile of railway track using vehicle-based inertial readings. P I Mech Eng F: J Rai2016; 231: 518–534.
29.
QuirkePOBrienEJBoweCet al. Calibration of railway vehicle model using measured inertial response ofin-service train for use in drive-by track monitoring system. Mech Syst Signal Pr [Preprint], 2017.
30.
RubinsteinRKroeseD.The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning. New York: Springer-Verlag, 2004.
31.
DowlingJOBrienEJGonzálezA.Adaptation of cross entropy optimisation to a dynamic bridge WIM calibration problem. Eng Struct2012; 44: 13–22.
32.
BelayAOBrienEJKroeseD.Truck fleet model for design and assessment of flexible pavements. J Sound Vib2008; 311: 1161–1174.
33.
HarrisNKGonzálezAOBrienEJet al. Characterisation of pavement profile heights using accelerometer readings and a combinatorial optimisation technique. J Sound Vib2010; 329: 497–508.
34.
OBrienEJKeenahanJ.Drive-by damage detection in bridges using the apparent profile. Struct Control Hlth2015; 22: 813–825.
35.
NtotsiosEThompsonDHusseinM.The effect of track load correlation on ground-borne vibration from railways. J Sound Vib2017; 402: 142–163.
36.
CanteroDArvidssonTOBrienEJet al. Train–track–bridge modelling and review of parameters. Struct Infrastruct E2016; 12: 1051–1064.
37.
QuirkeP.Drive-by detection of railway track longitudinal profile, stiffness and bridge damage. Dublin: University College Dublin, 2017.
