# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 1 Introduction  
The performance and safety of railway systems are significantly affected by railway point machines (RPMs) [1], one of the most critical pieces of equipment in railway signaling systems. An RPM consists of an electric motor and mechanical assembly, which move, detect, and lock the end position of the point tongue either individually or following the direction of the route formed. According to some relevant studies, over the years, failures in RPMs have been the most frequent cause of faults in railway signaling systems, which has a significant impact on the efficiency and safety of train operation [2; 3; 4]. In addition, at present, conventional maintenance for RPM equipment is mostly employed by railway operators, utilizing large amounts of manpower and material resources [5]. In order to optimize the effectiveness of machine operation, reduce unplanned downtime, and decrease operational and maintenance expenses, it becomes imperative to create an intelligent fault diagnosis (FD) method for RPM equipment. The method should be designed to evaluate the health status, aiming to identify the type, severity, and degradation trend of potential faults [6]. The FD method also has a big impact on increasing the quality and efficiency of maintenance by providing appropriate maintenance recommendations based on the current state of the equipment [7]. In a dynamic system, there are three main methods of fault diagnosis [8]: model-based fault diagnosis, knowledge-based fault diagnosis, and data-driven-based fault diagnosis. Model-based fault diagnosis compares a measured signal with the actual process output and its estimation generated in terms of an appropriate mathematical model of the system working under normal operating parameters [8]. Knowledge-based fault diagnosis focuseson a massive number of historical data to construct a knowledge base that explicitly represents the dependency of system variables [8]. Data-driven fault diagnosis derives valuable information from data captured by sensors and actuators in a dynamic system [9]. In recent years, data-driven fault diagnosis for railway point machines has received increased attention [10] because there is no requirement to model the system. There are two methods used in data-driven-based fault diagnosis: statistical and machine learning methods [8]. The machine learning method has many advantages over the statistical method, including its ability to learn the entire system's behavior with a few datasets [8], describe very complex and non-linear systems with great accuracy in defect identification, and discover new issues or errors with insufficient data [8]. In recent years, various sensors, including voltage [11], current [11], force [11], acoustic [2], and vibration [12], have been employed in the FD method for RPM equipment. The implementation of a current sensor is the most practical since it does not interfere with operations and can be installed in the power supply, which is located inside the equipment room in the station area, rather than inside the RPM equipment or field area [11]. Furthermore, electric current data can be used to diagnose not just electrical but also mechanical failures [13]. Several studies have shown promising outcomes in utilizing this method for diagnosing faults in railway point machines. For instance, [PERSON] et al. [11] applied wavelet transforms and Support Vector Machines (SVMs) to detect RPM faults using voltage and current data, and the experimental results demonstrated 100% cross-validation accuracy. [PERSON] et al. [14] successfully combined the self-organizing map-minimum quantization error and principal component analysis T\({}^{2}\) for classifying RPM conditions utilizing voltage and current data, achieving 81% classification accuracy in fault detection. [PERSON] et al. [15] experimented to determine whether it was worth replacing RPM using current-data and shapelet methods, achieving 97% accuracy for balanced data and 95% accuracy for unbalanced data in fault detection. [PERSON] et al.