Labelling the State of Railway Turnouts Based on Repair Records

Turnouts are the most expensive part to maintain on the railway track and therefore automated systems for detecting turnout defects are of great interest. Machine learning can improve predictive maintenance and is often used in automatic systems for precise prognosis. In this study, machine learning is used for identifying the condition of railway turnouts and potentially reducing costs by early automatic detection of defects.

To train a machine learning algorithm, ordered, structured and categorized data (labelled data) are needed. A method is proposed to label the condition of turnouts in the Danish Railway based on a collection of repair records. This labelling of the turnouts is accomplished with unsupervised methods, namely a principal component analysis (PCA) followed by a cluster analysis.

The labelling of the turnouts is investigated through comparisons of geometric measurements captured from the recording car. The difference in the physical properties illustrated by the geometric data indicates that the labelling is a good indicator of the relative condition of the turnout. When the data are labelled, supervised learning can be used to optimize the predictive power of machine learning algorithms (i.e. the algorithm learns from the labelled data) for classification of turnouts.