The reliability and safety of the train system is a critical issue, as it transports many passengers in its daily operation. Most studies focus on fault diagnosis methods to determine the cause of faults in the train system. Aside from fault diagnosis, it is also vital to perceive a fault even before it occurs. In this study, a fault occurrence prediction based on a machine learning model is developed. The fault occurrence prediction method aims to predict the remaining useful life (RUL) of a train subsystem. RUL refers to the remaining amount of time before a fault occurs on a train subsystem. The prediction method developed in this study can be used to clear a fault even before it occurs. In case of inevitable faults, the output from the prediction method can be used to alert the personnel in charge by imposing an alarm. Therefore, the fault occurrence prediction method is expected to increase the reliability of the train system. The deep neural-network-based model is tested on an actual device. Deep neural network is used because of its feature extraction capability, especially in handling big amount of data. The testing results in 90.08% accuracy. In addition, a graphical user interface is developed as an interface between a user and the actual device containing the fault occurrence prediction model.
Deep-Learning-Based Fault Occurrence Prediction of Public Trains in South Korea
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2676 , 4 ; 710-718
2022-02-02
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
British Library Online Contents | 1997
SLUB | 2011
|Diesel trains for South America
Engineering Index Backfile | 1937
Diesel trains for South America
Engineering Index Backfile | 1937