# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 4 Summary and Concluding Remarks  
In this paper, a comprehensive overview and summary of the existing fault detection, diagnosis, and prognosis methods and their application to railway S&C systems have been presented. Thus, this paper first introduced the fundamentals of railway S&C systems, including their mechanisms, sensors and controllers, actuators, and locking systems, in addition to attempting to organize and classify the diverse failure modes of different railway S&C systems. These diverse failures were thus classified according to their source and nature into mechanical failures, electrical failures, construction failures, and functional failures. Each of these failure types can be found in one or several subsystems of railway S&C systems, including faults in actuators, faults in sensors and controllers, faults in the detection and locking parts, and faults in the permanent way (i.e., the rails and any other elements that support the vehicle). Then, the different FD, FDD, and FP methods for railway S&C systems were classified as either model-based or data-driven. Several key points can be concluded from this review:  
* Only six papers, as shown in Figure 18, were found that applied a model-based FDD method for railway S&C CMS, and only one paper dealt with FP using a model-based method. In addition, only a few possible fault scenarios of the S&C system were considered and tested for model-based FDD methods, mainly focusing on slide chair-related faults (i.e., dry, contaminated, excessive friction or resistance) and switch-related faults (such as stuck, bad contact with the base plate, idle, resistance, hard to release, cannot lock, or misalignments). For the PHM application in S&C systems, only a contaminated slide chair fault scenario was studied when applying a model-based method. Considering the large number of published papers and the amount of research work done in modeling a whole railway S&C system or only part (subsystem) of it, the authors anticipated finding several if not dozens of papers applying a model-based FDD method to different railway  
Figure 21: Frequency of applying signals in the field of FDD for railway S&C systems. S&C systems. However, only a few papers (seven in total) were found. This is due to the several difficulties that researchers can face when trying to apply a model-based FDD method for railway S&C CMS:  
* the complexity of S&C systems that comes from their ramified structures makes finding an accurate enough reference model challenging. * the amount of potential failure modes and their predictability. * the different drawbacks of model-based techniques such as the fact that the accuracy of the reference model developed directly affects performance of the fault detection, diagnosis, and/or prognosis process; and that the construction of high-fidelity mathematical models from physical principles of such a complex system (i.e., railway S&C system) can become very complicated, time-consuming, and even sometimes unfeasible. * development of the model requires much prior knowledge (e.g., physical, mechanical, or electrical laws) about the real system. * It is well known in the CMS field that if researchers can construct an accurate enough reference model, using a model-based FDD method for fault detection, diagnosis, and/or prognosis is the best choice. Thus, building and merging dynamic models of a railway S&C system as a whole or for each of its subsystems could enhance the performance of model-based FDD methods for S&C systems. However, because of the complexity of the S&C system as a whole and the diversity of its components as well as its failures, it is recommended that researchers consider incorporating those dynamic models for each of its subsystems, one by one at first, and then integrating them into a more complete model if possible.