Efeoglu, EbruTuna, Gurkan2024-06-122024-06-1220221052-61881934-9394https://doi.org/10.3103/S1052618822050041https://hdl.handle.net/20.500.14551/23290Predictive maintenance relies on machine learning techniques to learn from historical data and also uses live data to analyse failure patterns. Different from conservative maintenance procedures that generally lead to resource wastage, predictive maintenance can offer optimum resource utilisation and allow predict failures before they occur. Machine learning techniques are essential for automated predictive maintenance; therefore, in this paper the use and effectiveness of support vector machines for predictive maintenance is analysed. As the results show, support vector machines achieve the best performance when linear kernel function is used.en10.3103/S1052618822050041info:eu-repo/semantics/closedAccessPredictive MaintenanceSupport Vector MachinesKernel FunctionsConfusion MatrixAccuracyArticle515447456N/AWOS:0008653903000092-s2.0-85139781837Q3