Machine learning-aided identification of train weights from railway sleeper vibration

Abstract

Within the UK and many other countries, the weight of a train is a critical factor in determining how much the train operator must pay to use the railway network. This study explores the use of different machine learning algorithms to predict the weight of a train based on the vibration signals recorded in a railway sleeper. The vibration signals are split into two groups: time-domain and frequency-domain signals. Then, different algorithms are developed for each group, to see which domain offers the best representation of a vibration signal for use within models to determine target values. From the study, it can be seen that machine learning has the potential to predict the weight of a train effectively. This insight can lead to the use of mobile sensors in practice, such as the application of wireless accelerometers connected with a smartphone, to help engineers audit and provide assurance of the train access integrity.