# 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  
To the authors' knowledge, prediction of the RUL of an S&C system using a data-driven method was first performed by [PERSON] et al. [PERSON] et al. (2018). An artificial neural network (ANN)-based technique was used as a learning algorithm. More precisely, a time-delay neural network (TDNN) method was used for estimating and predicting future values of railway S&C system parameters based on past and current information. A TDNN was chosen because its input structure incorporates time information, which makes it suitable for time-related problems such as prediction. An electromechanical railway S&C system was considered, and different sensors were installed (i.e., force sensor, linear ruler sensor, proximity sensor, and motor current sensor) and their signals analyzed. The analysis revealed that the force sensor contained the most valuable information about the health of the turnout system until failure, as can be seen in Figure 16. However, analyzing closely the results shown in Figure 16, it should be noted that the significant increase in force signal in Figure 16b appeared outside of the time window represented in Figure 16a, thus further analysis/work should be considered with consistent data and results. [PERSON] et al. [PERSON] et al. (2018) presented a simple state-based prognostic (SSBP) method for detecting and forecasting the health condition of an electromechanical S&C system. Then, it was compared to a hidden-Markov model-based method on real data collected from a railway S&C system. Basically, the proposed SSBP method had three steps: (1) clustering that was achieved based on the \(k\)-means clustering algorithm; (2) cluster evaluation in which a Calinski-Harabasz (CH) index Calinski and Harabasz (2018) was chosen for validation (it was used to give the optimal number of clusters and health states); (3) finally, RUL prediction was achieved using the transition probabilities between health states. In this study, nine sensors were installed in the S&C system to collect the force, current, and voltage signals, in addition to two proximity sensors for each rail, and two displacement sensors for each drive rod. Eighty percent of the collected data was used to train the proposed RUL prediction algorithm (i.e., the SSBP method) and the remaining 20% was left for testing and validation. The root mean square error (RMSE) and \(r\)-square values between the real and estimated RUL were used as measures to evaluate the effectiveness of the prognostic method. Despite the proposed SSBP method's simplicity, the results showed that it outperforms the hidden-Markov model-based prediction method. However, it had a restriction on state transitions, allowing transition of a state only to itself and a consecutive state, thus it ignored the effect of duration. Therefore, [PERSON] et al. [PERSON] et al. (2018) improved their work, deploying a state-based prognostics with duration information (SBPD) algorithm to identify the health state first before calculating the RUL of the railway S&C system. Figure 16: Two signals with failure progression from healthy to close to failure states: (**a**) current signal, (**b**) force signal [PERSON] et al. (2018). Other works dealing not only with S&C FD, but also with S&C FP are presented in [125; 126; 127; 128]. In [125], contrary to the aforementioned papers, the acquired signals were the motor shaft speed and torque instead of the current and force signals. In addition, the complete maneuver of an electromechanical S&C system was divided into seven phases, as can be seen in Figure 17, instead of three phases when using current curves, as shown in Figure 14. Each phase was defined as follows: P1 is starting of the point machine motor, P2 is movement of the internal components to unlock the locking system, P3 is unblocking of the locking system, P4 is rail movement, P5 is blocking of the locking system, P6 is locking of the locking system, and P7 is the end phase.