PSI - Issue 54

Francisco Afonso et al. / Procedia Structural Integrity 54 (2024) 545–552 Francisco Afonso / Structural Integrity Procedia 00 (2019) 000 – 000

552

8

5. Conclusions Two monitoring systems for the railway track and wheel were successfully developed and tested in a laboratorial setting, the train wheel system was also tested in its operational maintenance scenario. Both the railway track and train wheel surface defect detection systems allow defining the surface defects by adjusting the tolerances of different metrics, successfully detect surface defects and warn the technician when such a detection occurs. Additionally, the railway track surface defect detection system successfully communicates any detected defects with the maintenance platform. Acknowledgements This work was developed in the scope of the project FERROVIA 4.0, nº 46111 which has received funding from “ANI - Agência Nacional de Inovação, S.A” through the programme “Mobilizador COPROMOÇÃO_PT2020”. References Alemi, A., Corman, F., & Lodewijks, G. (2017). Condition monitoring approaches for the detection of railway wheel defects. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 231(8), 961 – 981. Altuntas, C. (2021). Triangulation and time-of-flight based 3D digitisation techniques of cultural heritage structures. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B2-2021), 825 – 830. Berkovic, G., & Shafir, E. (2012). Optical methods for distance and displacement measurements. Advances in Optics and Photonics, 4(4), 441. Castillo-Mingorance, J. M., Sol-Sánchez, M., Moreno-Navarro, F., & Rubio-Gámez, M. C. (2020). A critical review of sensors for the continuous monitoring of smart and sustainable railway infrastructures. Sustainability (Switzerland), 12(22), 1 – 20. Davari, N., Veloso, B., Costa, G. de A., Pereira, P. M., Ribeiro, R. P., & Gama, J. (2021). A survey on data-driven predictive maintenance for the railway industry. In Sensors (Vol. 21, Issue 17). MDPI AG. Falamarzi, A., Moridpour, S., & Nazem, M. (2019). A Review on Existing Sensors and Devices for Inspecting Railway Infrastructure. Jurnal Kejuruteraan, 31(1), 1 – 10. Jing, G., Qin, X., Wang, H., & Deng, C. (2022). Developments, challenges, and perspectives of railway inspection robots. Automation in Construction, 138. Podofillini, L., Zio, E., & Vatn, J. (2006). Risk-informed optimisation of railway tracks inspection and maintenance procedures. Reliability Engineering and System Safety, 91(1), 20 – 35. LMI Technologies. (2022). Gocator 2300 Series. Zebra Technologies. (2023). Zebra Altiz Product Spec Sheet. Yang, C., & Létourneau, S. (2005). Learning to predict train wheel failures. KDD, 516 – 525. Ye, J., Stewart, E., Zhang, Di., Chen, Q., Thangaraj, K., & Roberts, C. (2021). Integration of Multiple Sensors for Noncontact Rail Profile Measurement and Inspection. IEEE Transactions on Instrumentation and Measurement, 70.