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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Identifying usage and stress patterns in 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 Chapter 3, methods have been proposed that facilitate a load spectrum based distinction between vehicles with and those without a failure of a hybrid component, whereas algorithms that allow a visual detection of structure such as clusters in the data have been discussed in Chapter 4. However, a common disadvantage of the approaches, which performed best on the studied datasets, is that they are all “black box” models, i.e., they do not allow to gain interpretable insights into the analytical relationship between the data and the obtained results. More precisely, it remains unknown which patterns in the data provoke the classifier rf Gini to assign a certain label to an instance on the one hand, and which ones induce particular objects to form a cluster in the low-dimensional maps, produced by method RF-t-SNE, while others do not, on the other hand.