# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 5 Conclusions  
In this research, we proposed a fault diagnosis approach based on current signals and machine learning methods for railway point machines. In this approach, the collected current signals from the test-bench experiment are converted into time-series data. Then, time-domain feature extraction based on scalable hypothesis testing is utilized to select the relevant features for the classifications. Finally, the selected features are employed in machine learning modeling to determine which ML algorithms perform the best. The research results demonstrate that the LR and kNN algorithms can achieve high accuracy, albeit lower than 90%. Furthermore, the SVM, DT, RF, and ANN algorithms show great accuracy (higher than 90%) in the fault diagnosis of railway point machines, especially the DT algorithm, which achieves a testing accuracy of 100% in classifying RPM equipment. This study has some limitations. For example, the data were collected via a test bench, rather than in the field; the current-monitoring system was not developed using industrial data acquisition; and the number of multi-classifications can be further increased. However, the success achieved when using one current sensor, which has low installation costs for the proposed fault diagnosis method, will be a key consideration for maintenance teams of railway companies. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Item** & **Method A**[15] & **Method B**[16] & **Proposed Method** \\ \hline Signal & RPM current & RPM current & RPM current \\ Dataset & Field data & Field data & Test bench \\ Task & RPM replacement & Fault detection & Fault diagnosis \\ Classification & Binary class & Binary class & Multi-class \\ Feature extraction & Shapelet & Stacked autoencoders & Time-domain feature extraction based on scalable hypothesis testing \\ Classifier & Shapelet & Stack autoencoders & Decision Tree \\ Accuracy & 97\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed method and previous works. Implementing the current-based fault diagnosis method for railway point machines in real-world railway operations requires several steps, such as sensor installation, data collection, data transmission to the server, data processing, integration with the monitoring system, and an alert mechanism. The potential challenges and barriers to its adoption will be technically complex for maintenance teams due to sensor reliability and cost implications, and the fault diagnosis system must meet industry requirements. Scaling up the implementation of current-signal-based fault diagnosis for railway point machines across a larger railway network will require several considerations such as sensor network architecture, communication architecture, big data management, robust algorithms, and big data interpretability. There are several strategies for overcoming the scalability issue, for instance, pilot testing, incremental implementation, and continuous improvements. Further research into combining data from other sensors, such as vibration and acoustic sensors, is also needed. A potential benefit of combining data from vibration and acoustic sensors is that both sensors could monitor the railway point machine all the time, even when the railway point machine is not in operation, whereas current sensors only monitor the railway point machine when it is in operation. Furthermore, the use of both sensors would increase the availability of condition monitoring. Additionally, combining current, vibration, and acoustic sensors would give more information about the machine's conditions, especially in terms of electrical-mechanical parameters, resulting in a comprehensive fault diagnosis model. However, the potential drawback of combining data from vibration and acoustic sensors is that the installation must be placed in a railway point machine, which is not easy in a narrow RPM space. The installation must comply with the safety of the machine's operation.