# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems
### Model-Based FDD Methods for Railway S&C Systems
#### 3.1.1 FD Methods  
[PERSON] et al. [74] proposed an unobserved components class of model that was set up in a state space framework to deal with monitoring (detection) of the main wear-related failures of S&C  
Figure 10: Model-based FDD method for S&C system.  
mechanisms. A Kalman filter (KF) with fixed interval smoothing (FIS) algorithms was used for state estimation, and the maximum likelihood (ML) approach was used to estimate the unknown parameters of the system. For FD, they simply considered the absolute values of the difference between the current and reference data. The authors considered different wear-induced faults, including dry slide chairs, lubricated slide chair, and backdrive overdriving at the heel on normal and reverse sides with dry and lubricated slide chairs. The model-based FD method developed was tested in off-line mode with data collected from laboratory tests. Both force signal and DC motor current signal were collected as the output signals, whereas the supply voltage was considered for the input signal (it was considered constant). Their results showed that their model-based FD method is able to detect the main wear-related faults in both operating directions of the S&C system considered. The authors did not consider fault diagnosis in this work.  
A modified Bayesian network model combined with Monte Carlo simulations for the prediction of weather-related failures in railway S&C systems was developed by [PERSON] et al. [75]. The framework of their S&C failure prediction method is shown in Figure 11. In this study, the authors directly considered S&C system failure instead of faults because their exact causal relationship has not been yet established in the literature [10]. Date, minimum and maximum temperature, atmospheric pressure, weather phenomena (including rain, showers rain, snow, sleet, thunderstorm, etc.), and wind speed and its direction were considered as the input data. Some predicted failure outputs considered were: the switch slide chair lacking oil because of a rainstorm; the switch being stuck by snow; switch rail turnout stretching because of temperature change; and the switch rail having a bad contact to the base plate in the normal position.  
The authors, [PERSON] et al. [76], recently proposed a model-based FD method that considered a modified way of computing the residuals between the real system output(s) and the reference model output(s) with an application to an electromechanical switch system. The simulation model (reference model) was developed in Matlab/Simulink, where the input was the power supply voltage and the outputs were the motor speed [rad/s], the motor stator current [A], the force [N], and the linear position of the switch rails (i.e., the switch toe position) [m]. The modified accumulative residual based FD method performance and effectiveness were demonstrated using a set of different fault levels for the excessive friction or resistance fault in the electro-mechanical switch system. Their results showed that the considered fault was able to be detected using motor speed signal, motor stator current signal, force signal, and/or linear position of the switch rails with tendency to be implemented in the real rail network.  
Figure 11: Framework for weather-related failure prediction in railway S&C systems [75].