Generative Adversarial Network-based Missing Data Handling and Remaining Useful Life Estimation for Smart Train Control and Monitoring Systems

Abstract

As railway is considered one of the most significant transports, sudden malfunction of train components or delayed maintenance may considerably disrupt societal activities. To prevent this issue, various railway maintenance frameworks, from “periodic time-based and distance-based traditional maintenance frameworks” to “monitoring/conditional-based maintenance systems,” have been proposed and developed. However, these maintenance frameworks depend on the current status and situations of trains and cars. To overcome these issues, several predictive frameworks have been proposed. This study proposes a new and effective remaining useful life (RUL) estimation framework using big data from a train control and monitoring system (TCMS). TCMS data is classified into two types: operation data and alarm data. Alarm or RUL information is extracted from the alarm data. Subsequently, a deep learning model achieves the mapping relationship between operation data and the extracted RUL. However, a number of TCMS data have missing values due to malfunction of embedded sensors and/or low life of monitoring modules. This issue is addressed in the proposed generative adversarial network (GAN) framework. Both deep neural network (DNN) models for a generator and a predictor estimate missing values and predict train fault, simultaneously. To prove the effectiveness of the proposed GAN-based predictive maintenance framework, TCMS data-based case studies and comparisons with other methods were carried out.

Real-Time Hybrid Deep Learning-Based Train Running Safety Prediction Framework of Railway Vehicle

“…Similarly, Yang et al [18] applied the ResNet model [19] to determine rail defects. Lee et al [20] applied a generative adversarial network (GAN) approach to estimate the remaining life of train components to detect faults in trains. However, these studies have applied deep learning methodologies to image-based risk detection or train data analyses.…”

A Genetic Algorithm and PCA-Based Feature Selection to Improve the Failure Diagnosis Performance of Railway Vehicle Doors

“…Recent advances in communication technology, large amounts of data in onboard computer of railway vehicle can be transmitted to ground computers wirelessly, increasing the number of research related to ML and AI of door failure classification [19,20]. In addition to this, the time series data from key devices requiring reliability can also be used to trace deterioration by using regression techniques to predict a remaining useful life of parts [21,22].…”