Integration of on-board monitoring data into infrastructure management for effective decision-making in railway maintenance

Abstract

Railway infrastructure is a crucial asset for the mobility of people and goods. The increased traffic frequency imposes higher loads and speeds, leading to accelerated infrastructure degradation. Asset managers require timely information regarding the current (diagnosis) and future (prognosis) condition of their assets to make informed decisions on maintenance and renewal actions. In recent years, in-service vehicles equipped with on-board monitoring (OBM) measuring devices, such as accelerometers, have been introduced on railroad networks, traversing the network almost daily. This article explores the application of state-of-the-art OBM-based track quality indicators for railway infrastructure condition assessment and prediction, primarily under the prism of track geometry quality. The results highlight the similarities and advantages of applying track quality indicators generated from OBM measurements (high frequency and relatively lower accuracy data) compared to those generated from higher precision, yet temporally sparser, data collected by traditional track recording vehicles (TRVs) for infrastructure management purposes. The findings demonstrate the performance of the two approaches, further revealing the value of OBM information for monitoring the track status degradation process. This work makes a case for the advantageous use of OBM data for railway infrastructure management, and attempts to aid understanding in the application of OBM techniques for engineers and operators.

References

[1.] Nerlich I, Holzfeind J, Wilczek K. SwissTAMP—big data in proactive track asset management. Eur Railw Rev, 2016, 2: 41-44

[2.] Ding SH, Kamaruddin S. Maintenance policy optimization—literature review and directions. Int J Adv Manuf Technol, 2015, 76(5): 1263-1283.

[3.] Sheut C, Krajewski LJ. A decision model for corrective maintenance management. Int J Prod Res, 1994, 32(6): 1365-1382.

[4.] Sharma RK, Kumar D, Kumar P. FLM to select suitable maintenance strategy in process industries using MISO model. J Qual Maint Eng, 2005, 11(4): 359-374.

[5.] Mechefske CK, Wang Z. Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies. Mech Syst Signal Process, 2001, 15(6): 1129-1140.

[6.] CEN (2008) EN 13848-5: Track - Track geometry quality - Part 5: Geometric quality levels - Plain line, switches and crossings.

[7.] SBB, CFF, FFS (2009), Einbau, Kontrollen und Unterhalt von Gleisen, R I-22070, Infrastruktur SBB

[8.] Ngamkhanong C, Kaewunruen S, Costa B. State-of-the-art review of railway track resilience monitoring. Infrastructures, 2018, 3(1): 3.

[9.] Zonta T, da Costa CA, da Rosa RR, et al.. Predictive maintenance in the Industry 4.0: a systematic literature review. Comput Ind Eng, 2020, 150: 106889.

[10.] Soleimanmeigouni I, Ahmadi A, Kumar U. Track geometry degradation and maintenance modelling: a review. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2018, 232(1): 73-102.

[11.] Falamarzi A, Moridpour S, Nazem M. A review of rail track degradation prediction models. Aust J Civ Eng, 2019, 17(2): 152-166.

[12.] Jeong MC, Lee SJ, Cha K, et al.. Probabilistic model forecasting for rail wear in Seoul metro based on Bayesian theory. Eng Fail Anal, 2019, 96: 202-210.

[13.] Faiz RB, Singh S (2009) Time based predictive maintenance management of UK rail track. In: 2009 International conference on computing, engineering and information. Fullerton, CA, USA. IEEE, pp 376–383

[14.] Hao X, Yang J, Yang F, et al.. Track geometry estimation from vehicle–body acceleration for high-speed railway using deep learning technique. Veh Syst Dyn, 2023, 61(1): 239-259.

[15.] Sun X, Yang F, Shi J, et al.. On-board detection of longitudinal track irregularity via axle box acceleration in HSR. IEEE Access, 2021, 9: 14025-14037.

[16.] Marschnig S, Veit P (2015) SBB Standardelemente Gleise und Weichen.

[17.] Winklehner D (2015) Modell für die strategische Mengenprognose vom Substanzerhalt der Fahrbahn. ZEVrail.

[18.] CEN (2017) EN 13848–5:2017, Railway applications. Track. Track geometry quality. Geometric quality levels. Plain line, switches and crossings

[19.] Jodlbauer G, Neubert J. Digitales Aufzeichnungs system für Gleisbaumaschinen. EI-Eisenbahningenieur, 2014, 8: 22

[20.] Weinold T, Grimm-Pitzinger A. Die Lagerung der Gleisvermessungen der ÖBB. Vermessung & Geoinformation, 2012, 7(3): 348-352

[21.] Landgraf M (2016) Zustandsbeschreibung des Fahrwegs der Eisenbahn–Von der Messdatenanalyse zum Anlagenmanagement. Dissertation, TU Graz

[22.] Mermec Inc. (2023) Opto-inertial track geometry measurement system. https://www.mermecgroup.com/measuring-trains-br-and-systems/track-measurement/185/track-geometry.php. Accessed Oct 15 2023

[23.] Hoelzl C, Dertimanis V, Landgraf M, et al.. On-board monitoring for smart assessment of railway infrastructure: a systematic review. The rise of smart cities, 2022 Amsterdam Elsevier 223-259.

[24.] Lathe AS, Gautam A. Estimating vertical profile irregularities from vehicle dynamics measurements. IEEE Sens J, 2020, 20(1): 377-385.

[25.] Tsunashima H, Naganuma Y, Kobayashi T. Track geometry estimation from car-body vibration. Veh Syst Dyn, 2014, 52(sup1): 207-219.

[26.] Linke P, Hoksch DIV, Wolter K, et al.. Monitoring und Zustandsorientierte Instandhaltung von Schienenfahrzeugen und-fahrweg mittels Mustererkennung in Ereignisdaten. Tag Mod Schienenfahrzeuge, 2016, 140: 214-223

[27.] OBrien EJ, Bowe C, Quirke P, et al.. Determination of longitudinal profile of railway track using vehicle-based inertial readings. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2017, 231(5): 518-534.

[28.] Bocciolone M, Caprioli A, Cigada A, et al.. A measurement system for quick rail inspection and effective track maintenance strategy. Mech Syst Signal Process, 2007, 21(3): 1242-1254.

[29.] Hayashi Y, Kojima T, Tsunashima H et al. (2006) Real time fault detection of railway vehicles and tracks. In: 2006 IET international conference on railway condition monitoring, Birmingham, UK, 29–30 Nov. 2006. IET, pp 20–25

[30.] Li SG, Núñez A, Li ZL et al. (2015) Automatic detection of corrugation: preliminary results in the Dutch network using axle box acceleration measurements. In: Proceedings of the ASME/ASCE/IEEE 2015 joint rail conference, 23–26 March 2015, San Jose, California, USA. ASME, New York, pp 1–7

[31.] Hoelzl C, Ancu L, Grossmann H et al. (2022) Classification of rail irregularities from axle box accelerations using random forests and convolutional neural networks. In: Madarshahian R, Hemez F (eds) In: Data science in engineering, Volume 9: Proceedings of the 40th IMAC, a conference and exposition on structural dynamics 2022. Springer, Cham, pp 9197

[32.] Molodova M, Li Z, Dollevoet R. Axle box acceleration: measurement and simulation for detection of short track defects. Wear, 2011, 271(1–2): 349-356.

[33.] Molodova M, Oregui M, Núñez A, et al.. Health condition monitoring of insulated joints based on axle box acceleration measurements. Eng Struct, 2016, 123: 225-235.

[34.] Molodova M, Li Z, Núñez A, et al.. Parametric study of axle box acceleration at squats. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2015, 229(8): 841-851.

[35.] Dertimanis VK, Zimmermann M, Corman F et al (2020) On-board monitoring of rail roughness via axle box accelerations of revenue trains with uncertain dynamics. In: Barthorpe R (ed) In: Model validation and uncertainty quantification, Volume 3: Proceedings of the 37th IMAC, A conference proceedings of the society for experimental mechanics series. Springer, Cham, pp 167–171

[36.] Ágh C. Comparative analysis of axlebox accelerations in correlation with track geometry irregularities. Acta Tech Jaur, 2019, 12(2): 161-177.

[37.] CEN (2019) BS EN 13848 railway applications-track-track geometry quality–Part 1: characterization of track geometry. BSI Standards Publication.

[38.] Chudzikiewicz A, Bogacz R, Kostrzewski M, et al.. Condition monitoring of railway track systems by using acceleration signals on wheelset axle-boxes. Transport, 2018, 33(2): 555-566.

[39.] Head JD, Zerner MC. A Broyden—Fletcher—Goldfarb—Shanno optimization procedure for molecular geometries. Chem Phys Lett, 1985, 122(3): 264-270.

Funding

Eidgen?ssische Technische Hochschule Zürich