IMPLEMENTATION OF MACHINE LEARNING TECHNIQUES FOR PROGNOSTICS FOR RAILWAY WHEEL FLANGE WEAR
DOI:
https://doi.org/10.7166/31-1-2128Keywords:
Machine Learning, Railway Maintenance, Prognostics.Abstract
Machine learning has become an immensely important technique for automatically extracting information from large data sets. By doing so, it has become a valuable tool in various industries. In this investigation, the use of machine learning techniques for the production of railway wheel prognostics was investigated. Metrorail’s railway wheel wear data was used as a case study for this investigation. The goal was to demonstrate how machine learning can used on the data generated by Metrorail’s routine operations. Three machine learning models were implemented: logistic regression, artificial neural networks, and random forest. The investigation showed that all three models provided prognoses with an accuracy of over 90 per cent, and had an area under curve (AUC) measurement exceeding 0.8. Random forest was the best performing model, with an AUC measurement of 0.897 and an accuracy of 93.5 per cent.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish in the Journal agree to the following terms:- Authors retain copyright and grant the Journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this Journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the Journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this Journal.