# 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
Four papers used a shallow learning algorithm as an FD algorithm with application in railway S&C systems. The first one [96] was based on a one-class SVM (OCSVM) algorithm, the second paper [97] exploited a self-organizing map (SOM) technique, the third one [98] applied two machine learning classifiers, a Gaussian Naive Bayes (GNB) classifier and a neural network classifier (multilayer perceptron), and the fourth one [99] explored two recent machine/deep learning frameworks for classifying various switch degradation indicators including the featureless recurrent neural network called a Long Short-Term Memory (LSTM) architecture and the Deep Wavelet Scattering Transform (DWST). In the first paper, the OCSVM algorithm used a similarity measure of 'edit distance with real penalties' to classify the S&C health conditions as healthy or faulty. The current signal trends--collected from a real railway S&C system (i.e., infield data) instead of in-lab data (i.e., commonly researched laboratory-based data)--were considered as the analyzed features for the proposed FD method, and three obstructions faults, one contamination fault, and a crushed cable fault were investigated. The proposed OCSVM-based FD algorithm for railway S&C systems showed promising results compared with the commonly used threshold-based technique. However, it is limited in that it requires expert knowledge (which is very difficult and expensive to get) to label the data. In the second work, 16 features were extracted to be the inputs for the SOM technique. Mean value, root mean square value, standard deviation, slope, maximum value, and minimum value were extracted from the current and voltage signals that were coupled with information for maneuvering the railway S&C system as a whole (e.g., duration of the maneuver, area under the current curve, total power consumed, etc.) [97]. The authors in this research work considered two approaches: (1) a fleet-based approach based on the assumption that if several S&C systems are from the same manufacturer, are of similar age, and are installed in the same station, they can be assumed to belong to a uniform fleet and thus only one SOM (standardized) algorithm can be applied to the whole fleet equally; (2) an asset-based approach that, contrary to the former, assumes that the environment, field layout, and positioning influence the behavior of each S&C system, thus a dedicated SOM algorithm for each asset (i.e., S&C system) should be specifically trained and tested for this specific asset in a specific direction. Comparing both approaches in a 6-month case study on 20 point systems in a field setting, the results showed that the asset-based approach had more FD accuracy, but it was computationally more expensive and required a greater memory for storage. However, the results also showed that the fleet-based approach had fewer false alarms than the asset-based approach. The third paper [98], the applied two machine learning classifiers (the GNB classifier and the multilayer perceptron) were verified and tested in three different point machines, an electromechanical HW1000 in Belfast, an electromechanical Siemens 84M in Sydney, and a Hydraulic Mk3 Clamplock machine in London. They only measured the pressure (for the Clamplock) or current for the two others, where two fault modes were considered in this study, switch hard to release fault mode and fails to make detection, i.e., sensor fault mode. Finally, two recent machine/deep learning frameworks, the LSTM architecture and the DWST, were explored and they were evaluated for their feasibility on a dataset captured under the service conditions by the Alstom Corporation [99].