Prediction of mud pumping in railway track using in-service train data

Timely detection and identification of substructure defects in railway track are crucial for the safety and reliability of railway networks. Instrumented in-service trains can provide daily data for assessing the track conditions. This study tries to develop a data-driven model for the prediction of mud pumping defects using daily in-service train data. The data-driven model is based on long short-term memory (LSTM) networks. Bayesian optimization method is used to select the optimal hyper-parameters in LSTM. Genetic algorithm (GA) method is used for feature selection. A four-year real-world dataset from a section of railway network in Australia is used to train and test the data-driven model. The t-distributed Stochastic Neighbor Embedding (t-SNE) method is used to examine the resulting LSTM networks. The results show that the proposed approach can be used to predict the mud pumping defects in advance leaving enough time for maintenance.