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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Classifying component failures of a vehicle fleet
verfasst von : Philipp Bergmeir
Erschienen in: Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data
Verlag: Springer Fachmedien Wiesbaden
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In this chapter, the applicability of several state-of-the-art classification algorithms such as random forests and support vector machines are studied for the purpose of distinguishing non-faulty HEV from those suffering from a failure of a particular component of the hybrid power-train, when these algorithms are fed with load spectrum data. Furthermore, it is analysed whether these classifiers can be combined with feature selection approaches to not only improve the classification performance of the models, but also to select a small set of failure related features.