# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 4 Summary and Concluding Remarks
In addition, there are many model-based techniques that researchers have not used for railway S&C CMSs, and which are well known to be good solutions, such as parameter estimation methods (e.g., least square and its variants), state estimation methods (e.g., observer-based algorithms), and parity space methods (e.g., state space-based or input-output-based techniques. The benefit of considering hybrid techniques to better detect, diagnose, and/or prognose railway S&C system health conditions should not be forgotten. * In contrast to the lack of model-based FDD methods applied for railway S&C systems, a dozen research papers were found and reviewed in this paper that applied a data-driven FDD method for railway S&C systems because they are known to be more practical since there is no need to build a reference model (i.e., less prior knowledge is required). But they do need a large amount of (historical) data, basically from the output signal(s) to extract different features (indices, criteria), for synthesizing the information available from the raw data to better represent railway S&C system health conditions. * There are a huge number of features and data-driven FDD approaches, as reviewed by [PERSON] et al. [84], that researchers are encouraged to consider and investigate to extend the fault detection, diagnosis, and/or prognosis field with application to different railway S&C systems. These are not only limited to some of the data-driven FDD methods reviewed previously (in Table 2) such as the SVM algorithm and its variants, the PCA method, the WT technique and its variants, the SOM algorithm, and the DTW method. In particular, researchers could incorporate contemporary techniques--i.e., shallow learning-based PHM (SL-based PHM) and deep learning-based PHM (DL-based PHM) techniques--especially nowadays in the age of the Internet of Things (IoT) and big data [84]. * Although several fault scenarios have been considered when applying either a model-based FDD technique or a data-driven approach, almost all are related to wear-induced faults (i.e., slide chair-related faults and/or switch-related faults). However, other possible faults (as shown in Figure 9) could be also taken into consideration, especially the electrical fault modes. * The authors' analysis shows that there is a clear tendency toward applying specific signals (i.e., voltage, force, current, speed, and displacement) for FD, FDD, and FP of railway S&C system health conditions, with current signal most used for FD and FDD and force signal for FP. Analyzing these signals does indeed give good results; however, there is also an opportunity for researchers to adopt other signals such as the vibration signals that are sensitive to several faults, especially those related to any mechanical or structural deterioration of the system. * It is worth mentioning that it is beneficial if researchers also take into account the proposal and/or design of a complete new whole or part S&C model such as the REPOINT light track switch developed by Loughborough University, UK [132]. Innovative rail track switching technology has been developed and implemented on a test track, aiming to improve reliability and safety, reduce maintenance costs, and possibly reduce train delays in around 90% of point failures (2010-2013). Conceptualization, M.H. ; Writing--Original Draft Preparation, M.H. and S.D. ; Writing--Review & Editing, M.H., S.D., O.O., R.A., E.S., and R.D. ; Visualization, M.H. ; Supervision, E.S. and R.D. ; Project Administration, E.S. and R.D. ; Funding Acquisition, E.S. and R.D. Funding: The work described has been supported by the S-CODE project. This project has received funding from the Shift2 Rail Joint Undertaking under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 730849. This publication reflects only the authors' view, and the Shift2 Rail Joint Undertaking is not responsible for any use that may be made of the information it contains. The authors declare no conflict of interest.