Using Machine Learning as a Tool to Improve Train Wheel Overhaul Efficiency

This thesis develops a method for using machine learning in a industrial pro-cess. The implementation of this machine learning model aimed to reduce costsand increase efficiency of train wheel overhaul in partnership with the AustrianFederal Railroads, Oebb. Different machine learning models as well as categoryencodings were tested to find which performed best on the data set. In addition,differently sized training sets were used to determine whether size of the trainingset affected the results. The implementation shows that Oebb can save moneyand increase efficiency of train wheel overhaul by using machine learning andthat continuous training of prediction models is necessary because of variationsin the data set