Machine Learning for Predictive Maintenance: Support Vector Machines and Different Kernel Functions
Ebru EFEOĞLU, Gürkan Tuna
Abstract
Predictive 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.
Topics & Concepts
Predictive maintenanceSupport vector machineMachine learningKernel (algebra)Computer scienceArtificial intelligenceFunction (biology)Resource (disambiguation)Structured support vector machineData miningEngineeringReliability engineeringMathematicsBiologyEvolutionary biologyComputer networkCombinatoricsIndustrial Vision Systems and Defect DetectionMachine Fault Diagnosis TechniquesFault Detection and Control Systems