Abstract
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.
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Appendix: Clustering
Appendix: Clustering
1.1 Clustering Frequency for the Different Turnouts
The general cluster frequency for the turnouts can be seen in Fig. 8. Fewer turnouts are typically assigned to Cluster 1, but some turnout has a high frequency of Cluster 1. Typically a high frequency in Cluster 1 belongs to turnouts which are exposed to a larger tonnage (used more) and thereby more frequent maintenance is needed for these turnouts. Specifically it can be seen that the turnouts with the numbers of 1623, 5803, 5804 and 5805 (shown with the gray boxes) are more frequently assigned to Cluster 1.
Cluster 2 small community which could be seen in Fig. 8, the small community was especially separated in the third dimension (Dim.3). This community consist of two older turnouts with the numbers of 857 and number 5289 (shown with the black boxes) having campaigns captured in the period of 15,145 to 17,110 days after the renewal day. These two turnouts only had a few repairs and tamping/grindings take place in the defined time windows.
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Vassos, G., Hovad, E., Duroska, P., Thyregod, C., da Silva Rodrigues, A.F., Clemmensen, L.H. (2021). Labelling the State of Railway Turnouts Based on Repair Records. In: Galeazzi, R., Kjartansson Danielsen, H., Kjær Ersbøll, B., Juul Jensen, D., Santos, I. (eds) Intelligent Quality Assessment of Railway Switches and Crossings. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-62472-9_10
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