Deep-Learning-Based Fault Occurrence Prediction of Public Trains in South Korea

Abstract

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.

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

“…Slime mold populations rely on sensing the odor released by food in the air to search for the location of food. The specific method is detailed in Equation (12).…”

“…In contrast, data-driven methods avoid the cumbersome modeling process. This method uses historical data collected by monitoring systems as the research object [12], and conducts data analysis and processing, and uses relevant intelligent algorithms to establish trend prediction models, eliminating the influence of complex environmental changes on the trend of ship equipment status parameters. By establishing a unified standard trend prediction curve, engineers can assess the status of MDEs in advance by observing the trend changes in the EGT over a period of time, achieving real-time online monitoring of ships.…”

Real-Time Prognostics and Health Management Without Run-to-Failure Data on Railway Assets

“…J. Yan et al also developed a logistic regression-based approach for degradation evaluation and RUL prediction of elevator door open-close cycles [16]. More recently, A. Caliwag et al applied deep neural network techniques for fault occurrence prediction on the entire train system, including door systems [39]. Despite the successful outcomes of these approaches, the proposed RUL predictions still require enough RTF data to build the model.…”

Obstacle Detection Method of Underground Electric Locomotive Rail Based on Instance Segmentation

“…Insufficient illumination conditions and complex lines in coal mines have been considered major problems in conventional rail area detection. With the rapid advance of machine vision and deep learning, these problems can be solved using an intelligent rail transit auxiliary driving system based on computer vision (13)(14)(15)(16)(17)(18)(19)(20)(21).…”