System for the Acquisition and Analysis of Maintenance Data of Railway Traffic Control Devices
Source abstract
Abstract
A particularly important activity carried out by railway infrastructure managers to maintain railway devices in full working order is the diagnostic process. It increases the level of railway safety. The diagnostic process involves collecting information about the equipment through inspections, tests, functional trials, parameter measurements, and analysis of the working environment, followed by comparing the obtained information with the required parameters or permissible conditions. This activity also enables the formulation of a technical diagnosis regarding the current ability of the devices to perform its intended functions, taking into account the impact of its technical condition on railway traffic safety. This is especially important in the case of railway traffic control devices, as these devices are largely responsible for ensuring railway traffic safety. The collection of data on the condition of railway traffic control devices in the form of Big Data sets and diagnostic inference is an effective factor in making operational decisions for such devices. It enables the acquisition of complete information about the actual course of the exploitation process and allows for obtaining reliable information necessary to manage this process, particularly in the areas of diagnostics forecasting of devices conditions, renewal, and organization of maintenance and repair facilities. To support this, a service data acquisition and analysis system for railway traffic control devices (SADEK) was developed. This system can serve as a software platform for maintenance needs in the railway sector.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Transportation Systems and Safety · Advanced Research in Systems and Signal Processing · Railway Engineering and Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Control Systems and Electronics, Faculty of Transport, Electrical Engineering and Computer Science, Casimir Pulaski Radom University, 26-600 Radom, Poland
References (31)
Show source-provided references
1. Maintenance analytics for railway infrastructure decision support10.1108/JQME-11-2016-0059 ↗
2. Yang, N., Zhang, Y., Zuo, J., and Zhao, B. (2024). Fault Diagnosis Method for Railway Signal Equipment Based on Data Enhancement and an Improved Attention Mechanism. Machines, 12.10.3390/machines12050334 ↗
3. Victorino, T., and Peña, C.R. (2023). The Development of Efficiency Analysis in Transportation Systems: A Bibliometric and Systematic Review. Sustainability, 15.10.2139/ssrn.4355441 ↗
4. Kumar, U., Karim, R., Galar, D., and Kour, R. (2023). Risk-Based Safety Improvements in Railway Asset Management. International Congress and Workshop on Industrial AI and eMaintenance 2023, Springer.10.1007/978-3-031-39619-9 ↗
5. Laiton-Bonadiez, C., Branch-Bedoya, J.W., Zapata-Cortes, J., Paipa-Sanabria, E., and Arango-Serna, M. (2022). Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review. Sensors, 22.10.3390/s22072491 ↗
6. Recent applications of big data analytics in railway transportation systems: A survey10.1016/j.trc.2018.03.010 ↗
7. Rodríguez-Hernández, M., Crespo-Márquez, A., Sánchez-Herguedas, A., and González-Prida, V. (2025). Digitalization as an Enabler in Railway Maintenance: A Review from “The International Union of Railways Asset Management Framework” Perspective. Infrastructures, 10.10.3390/infrastructures10040096 ↗
8. A big data analysis approach for rail failure risk assessment
9. Zhang, L. (2016). Big Data Analytics for Fault Detection and Its Application in Maintenance. [Ph.D. Dissertation, Luleå University of Technology].
10. Pniewski, R., Kornaszewski, M., and Chrzan, M. (2023, January 4–6). Railway Traffic Control Systems Maintenance Modelling and Optimizing. Proceedings of the 27th International Scientific Conference “Transport Means 2023”, Palanga, Lithuania. Part I.
11. Preventive maintenance models–higher operational reliability
12. A taxonomy of railway track maintenance planning and scheduling: A review and research trends10.1016/j.ress.2021.107827 ↗
13. A decision support approach for condition-based maintenance of rails based on big data analysis10.1016/j.trc.2018.07.007 ↗
14. Railway Infrastructures E-Maintenance Systems: A Literature Review10.1088/1755-1315/1324/1/012045 ↗
15. General overview of maintenance strategies–concepts and approaches
16. Kornaszewski, M., Ciszewski, T., Nowakowski, W., and Pniewski, R. (2024). A Formal Model of the Exploitation Process for Railway Signalling Devices. Appl. Sci., 14.10.3390/app142311274 ↗
17. Improving railway maintenance schedules by considering hindrance and capacity constraints10.1016/j.trc.2021.103108 ↗
18. da Silva, R.F., Melani, A.H.d.A., Michalski, M.A.d.C., and de Souza, G.F.M. (2023). Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management. Appl. Sci., 13.10.3390/app131910605 ↗
19. Sresakoolchai, J., and Kaewunruen, S. (2023). Railway infrastructure maintenance efficiency improvement using deep reinforcement learning integrated with digital twin based on track geometry and component defects. Sci. Rep., 13.10.1038/s41598-023-29526-8 ↗
20. Predictive Maintenance Modelling for Through-Life Engineering Services10.1016/j.procir.2016.09.033 ↗
21. Preventive Maintenance Decisions through Maintenance Optimization Models: A Case Study10.1016/j.promfg.2017.07.241 ↗
22. Motaghare, O., Pillai, A.S., and Ramachandran, K.I. (2018, January 13–15). Predictive maintenance architecture. In Proceeding of the IEEE International Conference on Computational Intelligence and Computing Research, Madurai, India.10.1109/ICCIC.2018.8782406 ↗
23. Su, Z., Núñez, A., Baldi, S., and De Schutter, B. (2016, January 1–4). Model predictive control for rail condition-based maintenance: A multilevel approach. In Proceeding of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.10.1109/ITSC.2016.7795579 ↗
24. Predictive Maintenance for Railway Domain: A Systematic Literature Review10.1109/EMR.2023.3262282 ↗
25. An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition, planning and cost10.1016/j.trc.2018.02.010 ↗
26. Preventive and corrective maintenance–cost comparison and cost–benefit analysis10.1080/15732479.2015.1032983 ↗
27. Famurewa, S.M. (2015). Maintenance Analysis and Modelling for Enhanced Railway Infrastructure Capacity. [Ph.D. Dissertation, Luleå University of Technology].
28. IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry10.1016/j.autcon.2020.103486 ↗
29. Chellaswamy, C., Balaji, L., Vanathi, A., and Saravanan, L. (2017, January 10–12). IoT based rail track health monitoring and information system. In Proceeding of the International Conference on Microelectronic Devices, Circuits and Systems (ICMDCS), Vellore, India.10.1109/ICMDCS.2017.8211548 ↗
30. Mansour, M., Gamal, A., Ahmed, A.I., Said, L.A., Elbaz, A., Herencsar, N., and Soltan, A. (2023). Internet of Things: A Comprehensive Overview on Protocols, Architectures, Technologies, Simulation Tools, and Future Directions. Energies, 16.10.3390/en16083465 ↗
31. Nowakowski, W., Bojarczak, P., and Łukasik, Z. (2018, January 27–28). Railway automation systems diagnosis based on Bayesian method. In Proceeding of the IEEE International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering, ICRIEECE-2018, Bhubaneswar, India.10.1109/ICRIEECE44171.2018.9008871 ↗