# System for the Acquisition and Analysis of Maintenance Data of Railway Traffic Control Devices
## 7 Methods and Tools for Data Processing in the SADEK System  
Due to the wide variety of railway traffic control devices and systems (axle counter systems, interlocking systems, signaling block systems, and level crossing systems) and the technology in which they are implemented (mechanical, electromechanical, relay-based,  
Figure 4: General structure of the SADEK system. Figure 5: Block diagram of the fault decision-making process carried out at the OCC level. hybrid, and electronic devices), we decided to perform data collection and processing, including fault inference, at the OCC level. The SADEK system is designed to collect data from railway traffic control devices, i.e.,  
* Light signals (current flow in the bulb or LED circuit, control of the correctness of the displayed signal, lighting time, and response time to commands). * Switch drives (switching force based on current measurement, turnout switching time and number of switching cycles in a given period, turnout position, environmental parameters--temperature and humidity). * Track sensors and axle counters (number of detected axles and direction of vehicle movement, interface control, and detection of communication errors). * Control elements and modules, including relays (output voltages of actuators, status of relays, test results of safety circuits, environmental parameters, and system logs). The SADEK system is based on a maintenance strategy according to the technical condition and makes rational service decisions online based on the collected diagnostic data. For this purpose, using the model presented in the article, a classification of various types of technical objects was conducted, followed by the development of fault signatures. These signatures represent combinations of symptoms that indicate the occurrence of a fault for each of these objects. Using probabilistic reasoning and relying on historical data stored in Big Data collections together with the observed combinations of symptoms, possible faults are identified. For example, the conditional probabilities \(P(f_{k}\,|\,s_{j})\) of a \(f_{k}\) failure at an observed symptom of \(s_{j}\), assuming the independence of symptoms, can be determined using the following formula [31]:  
\[P(f_{k}|s_{1},s_{2},\dots,s_{j})=\frac{P(f_{k})P(s_{1}|f_{k})P(s_{2}|f_{k}) \cdots P(s_{j}|f_{k})}{\sum\limits_{i=1}^{K}P(f_{i})P(s_{1}|f_{i})P(s_{2}|f_{i })\cdots P(s_{j}|f_{i})} \tag{38}\]  
where \(K\) is the number of possible failures, and \(j\) is the number of failure signature elements. If the \(n\)-th symptom does not have to occur, according to the failure signature, then we insert it into Formula (38):  
\[P(\overline{s}_{n}|f_{k})=1-P(s_{n}|f_{k}) \tag{39}\]  
In equation (38), the left-hand side is the sought-after value of the a posteriori probabilities, while the right-hand side depends on the corresponding a priori probabilities, the value of which is mostly unknown. In the context of technical problems, the values of these probabilities can be estimated through estimation. Therefore, the values of the probabilities \(P(f_{k})\) and \(P(s_{j}\,|\,f_{k})\) can be estimated from random sample data:  
\[P(f_{k})=\frac{n_{f_{k}}}{n_{f}} \tag{40}\]  
where \(n_{f_{k}}\) is the number of \(f_{k}\) failures, and \(n_{f}\) is the number of all registered failures. \[P(s_{j}|f_{k})=\frac{n_{s_{j}\wedge f_{k}}}{n_{f_{k}}} \tag{41}\]  
where \(n_{s_{j}\wedge f_{k}}\) is the number of occurrences of \(s_{j}\) symptoms for \(f_{k}\) failures,  
\(n_{f_{k}}\)--number of \(f_{k}\) failures. The wheel sensor faults of the Level Crossing Protection System (LCPS) will be used as an example for further consideration.