Digital Twin for condition based maintenance within a railway infrastructure testing lab



Published Sep 4, 2023
Antonio J. Guillén López Juan Fco. Gómez Fernández Pedro Urda Jose Luis Escalona Adolfo Crespo Márquez Fernando Olivencia


This article presents a digital twin development in a railway case, in order to improve operations and maintenance decisions, aligned with an asset management strategy. A digital framework for the sustainable management of these assets is defined with the purpose of facilitating the implementation on a cloud platform, searching the generation and sharing of the produced models and the evaluation from different perspectives. The developed digital twin allows for the digital representation of railway lines and vehicles, the connection between different entities based on an ontology, the management of data ingestion and storage, and the administration of models for the detection, diagnosis, and prognosis, as well as the representation and control of the level of risk of the assets. When emulation of railway line degradation is searched, different types of data are combined, from on-board sensors in railway vehicles, and physical behaviours, up to machine learning algorithms for estimation. In this way, the degradation behaviour model for the railway line is shown and validated through intelligent models easily replicated in several areas of the railway network, showing risk levels for each one. 

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Digital Twin, Asset Management, CBM, Railway maintenance

Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021). Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model. Advanced Engineering Informatics, 47(December 2020).

Chamorro, R., Aceituno, J. F., Urda, P., del Pozo, E., & Escalona, J. L. (2022). Design and manufacture of a scaled railway track with mechanically variable geometry. Scientific Reports, 12, 8665.

Crespo Marquez, A., Gomez Fernandez, J. F., Martınez Galan Fernandez, P., & Guillen Lopez, A. (2020). Maintenance management through intelligent asset management platforms (iamp). emerging factors, key impact areas and data models. Energies, 13(15), 3762.

Fernandez, P. M.-G., Lopez, A. J. G., Marquez, A. C., Fernandez, J. F. G., & Marcos, J. A. (2022). Dynamicrisk assessment for cbm-based adaptation of maintenance planning. Reliability Engineering & System Safety, 223, 108359.

Ferrero Bermejo, J., Gomez Fernandez, J. F., Pino, R., Crespo Marquez, A., & Guillen Lopez, A. J. (n.d.). Review and comparison of intelligent optimization modelling techniques for energy forecasting and condition-based maintenance in pv plants. Energies, 12(21).

Gomez, J. F., Fernandez, P. M.-G., Guillen, A. J., & Marquez, A. C. (2019). Risk-based criticality for network utilities asset management. IEEE Transactions on Network and Service Management, 16(2), 755–768.

Gomez Fernandez, J., & Crespo Marquez, A. (2012). Maintenance management in network utilities. Framework and Practical Implementation. Sevilla: Springer.

Guillen, A. J., Crespo, A., Macchi, M., & Gomez, J. (2016). On the role of Prognostics and Health Management in advanced maintenance systems. Production Planning and Control. doi: 10.1080/09537287.2016.1171920

ISO. (2014). ISO 55000-1:2014 Asset management ? Overview, principles and terminology. , 7.

Karis, T., Berg, M., Stichel, S., Li, M., Thomas, D., & Dirks, B. (2018). Correlation of track irregularities and vehicle responses based on measured data. Vehicle System Dynamics, 56(6), 967-981.

Kraft, S., Causse, J., & Martinez, A. (2019). Black-box modelling of nonlinear railway vehicle dynamics for track geometry assessment using neural networks. Vehicle System Dynamics, 57(9), 1241-1270.

Lin, S.-W., Miller, B., Durand, J., Bleakley, G., Amine, C., Robert, M., . . . Crawford, M. (2019). The Industrial Internet of Things Volume G1 : Reference Architecture. Industrial Internet Consortium White Paper, Version 1., 58 Seiten.

Marquez, A. C., et al. (2022). Digital maintenance manage ́ ment. Springer Series in Reliability Engineering.

Munoz, S., Urda, P., & Escalona, J. L. (2022). Experimental ̃ measurement of track irregularities using a scaled track recording vehicle and kalman filtering techniques. Mechanical Systems and Signal Processing, 169, 108625. doi:

Rosa, A. D., Kulkarni, R., Qazizadeh, A., Berg, M., Gialleonardo, E. D., Facchinetti, A., & Bruni, S. (2021). Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 235(1), 107-120. doi: 10.1177/0954409720906649

Tsunashima, H., Naganuma, Y., & Kobayashi, T. (2014). Track geometry estimation from car-body vibration. Vehicle System Dynamics, 52(sup1), 207-219.

Urda, P., Aceituno, J. F., Munoz, S., & Escalona, ̃ J. L. (2021). Measurement of railroad track irregularities using an automated recording vehicle. Measurement, 183, 109765. doi:

Zheng, X., Lu, J., & Kiritsis, D. (2022). The emergence of cognitive digital twin: vision, challenges and opportunities. International Journal of Production Research, 60(24), 7610–7632.

Zio, E. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering and System Safety. doi: 10.1016/j.ress.2021.108119
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