# 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
#### 3.2.1 FD Methods  
[PERSON] et al. [86] applied three data-mining techniques (i.e., hypothesis testing, discriminant analysis, and interval estimation) to detect faults in an electric S&C machine. Both voltage and current signals were acquired for analysis. Their results showed that the first technique was able to detect the fault considered without fixing a pre-defined threshold. However, the hypothesis testing method results were not readable for maintenance technicians in the field; further, its performance decreased in the case of multiple faults. In addition, the authors found that the discriminant analysis technique was not capable of diagnosing faults in the electrical S&C system. However, they concluded that the third method (i.e., interval estimation) was more effective in terms of easier visible recognition and in terms of FD. Recording solely force against time, a statistical method using three criteria--irregularities in the signal shape, deviation of the maximum value position, and signature symmetry with respect to the maximum value position--was used for FD with application to S&C systems [87]. A United States patent was granted to [PERSON] et al. [88] who proposed a CM technique that detects and monitors the health condition of junctions, crossings, crossroads, or rail joints by means of a rail vehicle. The rail vehicle swing acceleration vibration, speed, and travel direction and position were collected to achieve the desired outcomes (i.e., S&C system CM). [PERSON] et al. [89] used an expert system for S&C system FD. Three health conditions were simulated to evaluate the performance of the proposed method: the analyzed signals were first preprocessed, using a moving-average smoothing algorithm, then a dynamic time warping (DTW) algorithm was used to find the optimal match for FDD of the considered fault modes in the S&C system. A method was developed for specific application in the detection of drive-rod out-of-adjustment failure mode in S&C systems, which was manually obtained [90]. It used support vector machines (SVM) with a Gaussian kernel for FD after feature selection and reduction with principal component analysis (PCA) [90]. The authors applied their CM method to an electromechanical S&C system with two drive rods. In addition, they acquired the linear position of the switch rails, the force signal, and the motor current signal. In this paper, only FD was considered. Several other published papers dealing only with FD for S&C systems were found [91; 92; 93; 94; 95; 96; 97]. In the work published by [PERSON] et al. [93], three electromechanical M63 (638, 642A, and 642B) and four electrohydraulic (639, 640, 641A, and 641B) S&C systems were monitored. FD was achieved using a harmonic regression algorithm combined with a vector autoregressive moving-average (VARMA) model to forecast the acquired signal (i.e., the current signal). Another work analyzed statistical--crest factor, impulse factor, kurtosis, max, mean, min, shape factor, and standard deviation--and energy--obtained from the energy of the signal in different segments--features extracted from the current signal for FD [94]. The analysis showed that the energy-based feature had more consistent trends than the others, thus they were used along with a PCA algorithm--based on the Hotelling T\({}^{2}\) coefficient--for detecting S&C system health conditions. An FD method for an electric switch machine for a railway S&C system was developed in [95]. The authors analyzed Kolmogorov-Smirnov (K-S) test results to detect three simulated switch machine faults: (1) slide chair friction, (2) a hard stone in the gap between the stock rail and switch blades, and (3) a soft stone in the gap between stock rail and switch blades. They extracted different time-domain stator current signatures in healthy and faulty cases to be analyzed by K-S test. These study results showed that the proposed K-S test method was not capable of classifying the three fault cases considered, which means that it is not sufficient for fault diagnosis purposes.