# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems
### Data-Driven FDD Methods for Railway S&C Systems
#### 3.2.3 FP Methods
For FP, the authors used the mean active power index that presented the movement of the blades--as it was found that was the phase most sensitive to several causes of degradation (mainly resistive forces)--and then a Monte Carlo simulation with a state representation method was performed to observe the degradation path and then estimate and predict the RUL of the railway S&C system. In 2016, an FP method based on a fault tree analysis approach, i.e., a binary decision diagram (BDD) approach, was proposed and applied to an M63 point machine [126]. The BDD approach used two importance measures (IMs)--the Birnbaum IM that overstates the importance of events with a low probability of occurrence, and the Criticality IM that takes into account the occurrence probability of events--to classify the events, and the AND method [129] to rank them. The FP method developed was used to set maintenance strategies that improve decision-making by optimizing resources. [PERSON] et al. [127] compared three of the previous FP methods applied to railway S&C systems (i.e., the TDNN method in [121], the SSBP method in [122], and the SBPD method in [124]). In addition, they also compared various sensors--force, current, voltage, proximity, linear ruler, rotary encoder motor (speed and position), string encoder gearbox output (position and speed)--in terms of their cost, ease of installation, reliability, and sensitivity to failure. The three prognostic methods were compared for their RUL estimation accuracy (performance) using the RMSE, \(r\)-square, prognostic horizon, and \(\alpha\)-\(\lambda\) accuracy metric indices. The comparison performed in this study concluded that: (i) failure degradation is better represented by force sensors (i.e., they are the best to be used as prognostic inputs), followed by linear ruler and current sensors, whereas proximity sensors are to be avoided for prognostic tasks; (ii) from a cost factor perspective, it is beneficial to use current, proximity, voltage, and speed sensors since they are cheaper, especially if, rather than being used directly, the raw data are processed and converted into force sensor values (if possible) that will make them better represent the failure degradation, which will improve the prognostic performance; (iii)  
Figure 17: Seven phases of the switch speed and power signals under normal operation condition (healthy) [125]. among the three prognostic methods, the SBPD RUL prediction method was the most accurate, whereas the TDNN RUL prediction method was the worst since it had huge fluctuations. After that, in 2017, [PERSON] [128] presented an approach to predict the RUL of railway switch engine failures that based on two supervised classification techniques, ANN and SVM. Only the power consumption of the engine collected through the SIDIS W (compact) condition monitoring system from Siemens was considered, to form a two year time series of 29 switches of Deutsche Bahn (with a S700K electromechanical engine). After preliminary analysis, the author discovered that using only the power signal was not sufficient and he considered in addition, the dependency of the switch condition data from climatic conditions (temperature and humidity) and certain properties of the switch construction type. To avoid common problems with uncertainty in measurement data, the author proposed a long prediction horizon (month) of small time units (hours). In addition, he studied the high number of possible parametrizations problem when applying his develop method and how it affect the RUL prediction.