# System for the Acquisition and Analysis of Maintenance Data of Railway Traffic Control Devices
## 9 Conclusions  
The collection and analysis of exploitation data from railway traffic control devices are fundamental organizational activities in the process of train traffic management. To obtain relevant diagnostic information, it is necessary to collect data in Big Data repositories and to continuously process and update operational data. Systems for the acquisition and analysis of service data for railway traffic control devices are playing an increasingly important role, improving the efficiency, safety, and reliability of the entire transport infrastructure. The utilization of contemporary Information Technology (IT) instruments facilitates uninterrupted observation of the technical status of railway infrastructure components. The real-time collection and analysis of data facilitates the early detection of anomalies, thereby enabling the scheduling of maintenance activities prior to the occurrence of major faults or accidents. The integration of maintenance support systems, underpinned by technologies such as the Internet of Things (IoT), Big Data, or artificial intelligence, facilitates the prediction of component deterioration, optimization of maintenance schedules, and reduction in recovery times. Moreover, they facilitate more precise resource planning, which may result in financial savings and greater availability of technical systems. Systems for the acquisition and analysis of service data in railway traffic control devices are also an important tool for management, providing access to up-to-date information on the condition of the railway infrastructure, which assists in making sound maintenance and strategic decisions. Consequently, the overall efficiency of railway undertakings and the level of service provided is enhanced. It is important to note that the development of this technology also promotes an increase in the level of safety, both by improving the reliability parameters of technical devices and by being able to react quickly in critical situations. Consequently, in an era characterized by escalating expectations of railway transport, computer-aided operation is emerging as an indispensable component of contemporary railway infrastructure management. In the current practice on the railway network in Poland, the operational data of traffic control devices are recorded manually in the form of EXCEL files, for small areas of the railway network. Interpretation of the database created in this way is cumbersome and time-consuming. The SADEK system automatically collects data and generates large data sets (Big Data), enabling real-time statistical analysis and enhancing the accuracy of operational decisions. This article proposed an original solution for the acquisition and analysis of service data from railway traffic control devices, called SADEK. This system performs, among others, the following functions:  
* Automatic collection of information from railway traffic control devices. * Exchange of information with external systems. * Archiving of information in a Big Data database. * Monitoring the current operational status of devices and systems. * Providing information to defined recipients. * Supervision of the maintenance process. * Reporting to the railway infrastructure manager regarding the reliability of devices and systems (for example, for estimating Life Cycle Cost). The SADEK system has a distributed structure. At the OCC level, data are collected in Big Data repositories and then used for probabilistic reasoning about potential failures. This information is ultimately sent to the MDC, where service decisions are made. The system provides a comprehensive solution for managing the exploitation process of railway traffic control devices and systems. The SADEK comprehensively solves problems in the management of the process of operation of rail traffic control equipment and systems. At the same time, due to its modular design, it can be expanded with new analytical functions, such as those supported by artificial intelligence, resulting from new needs. Conceptualisation, M.K. and W.N.