# 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  
As reviewed in the above subsection, several research works have been carried out in the area of FD technique development for railway S&C systems, in which the railway S&C systems' health conditions were monitored and categorized as healthy or faulty. However, only revealing if the system is healthy or faulty is not enough to achieve the development of an efficient CMS, even if it is an essential step. An FD technique only recognizes that a problem has occurred in the system, but it does not identify the root causes nor isolate the location of different faults (i.e., there is no isolation step). Thus, many researchers have developed different FDD techniques to deal with railway S&C system CM, and many papers can be found [11; 13; 15; 100; 101; 102; 103; 104; 105; 106; 107]; only selected papers are discussed in the following. It should be clearly noted that the only criteria applied for this selection were that only the most recent published papers were chosen (from 2017 onwards). The selected papers are just examples, and the selection does not indicate a preference or endorsement by the authors. However, a summary of all FDD methods for railway S&C systems and a list of all reviewed CM papers applied to S&C systems, with their application, method(s) used, signal(s) acquired, and the fault type(s) considered, is presented in Table 2.  
As an FDD method for railway S&C systems, [PERSON] et al. [113] proposed a signal-based CM technique that uses strain gauge signals to detect and diagnose changes in the combined system of the crossing nose geometry/wear state and the state of the underlying bedding in a railway S&C system. Those changes were due to wear and plastic deformation in service. In addition, a setup of laser-based non-contact 2D measurement of the crossing geometry has been considered to quantify the geometry change of the new crossing nose. With the aim of developing an intelligent FDD algorithm for railway S&C systems, a DTW method was proposed in [114]. Performance of the proposed data-driven FDD method was determined by analyzing the turnout current curves in healthy and five faulty cases: turnout suddenly stops running after starting, turnout jams, startup circuit disconnection, locking current exceeded, and automatic actuator is not flexible. Their results showed that the S&C system health conditions considered were automatically detected and then diagnosed with 100% accuracy. The authors achieved this high accuracy despite the sensitivity of DTW to the presence of noise and its level because they performed a curve normalization procedure (i.e., a preprocessing procedure) to eliminate the impact of noise. Further, the authors concluded that their proposed DTW-based FDD method for S&C systems could perform well even if the values of the current curve fluctuate and even if the operating time is different. In addition, they mentioned that their method will be suited to real-time CM since it does not need feature selection, historical data, or prior knowledge. [PERSON] et al. [115] proposed a hybrid FDD method for dealing with the CM of a railway S&C system. The hybrid FDD method was based on derivative DTW and a quartile scheme. Again, the hybrid FDD method analyzed the current curves collected by microcomputer monitoring systems; the switch operation current curves were divided into three phases, as can be seen in Figure 14. Several faults (e.g., abnormal fluctuation, start failure, open startup circuit, electric relay switch failure, etc.) were considered, and 20 features were used:  
* _Phase 1 (start stage, \(T_{1}\))_: time span, maximum value, mean current value, median current value. * _Phase 2 (action stage, \(T_{2}\))_: time span, maximum current value, minimum current value, mean, median, standard deviation, peak factor, fluctuation factor. * _Phase 3 (slow release stage, \(T_{3}\))_: time span, max current value, minimum current value, mean, median, standard deviation, peak factor, fluctuation factor.