Browsing by Author "Du Plessis, Johannes Andreas"

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    Implementation of machine learning techniques for railway wheel prognostics
    (Stellenbosch : Stellenbosch University, 2019-04) Du Plessis, Johannes Andreas; Fourie, Cornelius Jacobus; Van der Merwe, A. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: The Passenger Rail Agency of South Arica (PRASA) is in the process of moving from a mostly reactive to a preventive approach to maintenance. The key to cost-efficient preventive maintenance strategies is the ability to predict the condition of components at a future time. The objective of this research project was to ascertain whether machine learning techniques can be used to provide prognostic predictions with respect to the condition of PRASA’s railway and train components. The input data used to build the machine learning models was provided by Metrorail, a subsidiary of PRASA. Metrorail’s railway wheels were selected to serve as the case study for this project, owing to the fact that the condition monitoring data collected on the railway wheels represented the most granular and complete data set related to fluctuating conditions of a Metrorail train component. Five types of wheel wear are monitored by Metrorail. These forms of wheel wear are flange height increase, tread diameter decrease, hollow wear, flange slope increase and flange thickness decrease. Three machine learning models were built to provide prognostic predictions related to these types of wheel wear. These model types were logistic regression, artificial neural networks and random forest. One of each of these model types was developed for each of the wheel wear types. The performance of the models was then compared to ascertain which model performed the best for each of the wheel wear types. A normalised combination of sensitivity, specificity, F1 score and AUC was used to rank the models. Logistic regression was surpassed by the artificial neural network and random forest models for each of the wheel wear types. The artificial neural network was the best prognostic model for tread diameter decrease (accuracy: 96.4%, normalised score: 0.964). Random forest was the best prognostic model for flange height increase (accuracy: 93.5%, normalised score: 0.822), hollow wear (accuracy: 92.5%, normalised score: 0.731), flange slope increase (accuracy: 94.2%, normalised score: 0.953) as well as flange thickness decrease (accuracy: 92.9%, normalised score: 0.733). The encouraging results of these models showed that machine learning techniques can indeed be used to provide PRASA with train component wear prognostics. The models developed during the completion of this project can also be implemented by Metrorail to alleviate the need for manual wheel condition monitoring, by providing technicians with wheel prognostics.