# 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
#### 3.2.2 FDD Methods
Detection and diagnosis accuracy of 99.43% and 98.67%, respectively, was achieved, better than for SVM and DTW methods. Further, their results showed that the proposed hybrid FDD method for railway S&C systems was more robust since its false alarm rate and missing alarm rate were both low. Another FDD method that analyzed current curves for an S&C system was developed in [116]. It was based on a similarity function (i.e., Frechet distance) and a fuzzy c-means clustering method. The former was used for FD and the latter was used for fault diagnosis after using a k-means algorithm for curve selection. The developed two-stage (hybrid) FDD method was applied to a ZD6 turnout  
Figure 14: Three phases of switch current curve under normal operation conditions (healthy) [115]. located in Jinan Railway Station in China. The data analyzed were collected from 12 December 2017 to 10 January 2018, with a total of 817 curves for four turnouts (70% was used for training and the remaining 30% kept for validation). The authors concluded that their hybrid FDD method is suitable for real-time application since it is independent of human experience and can perform well, even under the influence of changing the location, weather, and working hours, especially since the FD accuracy is double-checked in the second stage. More recently, this year, [PERSON] et al. [117] applied a data-driven FDD method for railway S&C systems. First, they extracted the features--from the current and power signals--using a derivative method that segments the original sample. After that, they reduced the extracted features space using PCA and linear discriminant analysis (LDA) algorithms since they are the best statistical methods for space reduction [118; 119]. Finally, a modified SVM (i.e., balanced SVM) algorithm was used for fault classification and compared to the classical SVM for an imbalanced sample set. The results showed that the LDA + balanced SVM gave the highest detection and diagnosis accuracy, up to 99%. It is worth noting that the data-driven FDD method developed in this work was checked for several fault cases, including: \"mechanical jam\" fault (H1), \"improper position of the slide chair\" fault (H2), \"abnormal impedance in the switch circuit\" fault (H3), \"bad contact in the switch circuit\" fault (H4), \"abnormal open-phase protection device in the indicating circuit\" fault (H5), \"abnormal impedance in the indicating circuit\" fault (H6), \"the electric relay in the start circuit fails to switch\" fault (F1), \"supply interruption\" fault (F2), \"open-phase protection device\" fault (F3), \"fail to lock\" fault (F4), and \"indicating rod block in the gap\" fault (F5). The authors defined the labels as Hi (H1-H6) for hidden danger faults and as Fj (F1-F5) for major faults. In addition, they gave a common current curve for each fault type as can be seen in Figure 15.  
Further, this year, [PERSON] et al. [120] also applied a data-driven FDD method for railway S&C systems. A hybrid classifier that uses the partial system knowledge (partially known behavior) and a NN-based classifier, for comparison purposes, were considered with a set of faults in a rail switch system. The authors acquired force, speed, and power signals. They used the block-lower-triangular (BLT) form to determine the input and the output of the NN, where a deep learning framework was used to formulate the system dynamics. The authors considered four fault operating modes: left and right misaligned adjuster bolts, obstacle and missing bearings for validation. This method was designed to integrate the partial known physical information in the classifier design and the authors  
Figure 15: Current curve for each fault type [117]. demonstrated that the classification problems could be converted into a set of regression problems and a set of dimensionally reduced classification sub-problems.