Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data (eBook, PDF)
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Philipp Bergmeir works on the development and enhancement of data mining and machine learning methods with the aim of analysing automatically huge amounts of load spectrum data that are recorded for large hybrid electric vehicle fleets. In particular, he presents new approaches for uncovering and describing stress and usage patterns that are related to failures of selected components of the hybrid power-train. ContentsClassifying Component Failures of a Vehicle Fleet Visualising Different Kinds of Vehicle Stress and Usage Identifying Usage and Stress Patterns in a Vehicle Fleet Target Groups S...
Philipp Bergmeir works on the development and enhancement of data mining and machine learning methods with the aim of analysing automatically huge amounts of load spectrum data that are recorded for large hybrid electric vehicle fleets. In particular, he presents new approaches for uncovering and describing stress and usage patterns that are related to failures of selected components of the hybrid power-train.
Contents
- Classifying Component Failures of a Vehicle Fleet
- Visualising Different Kinds of Vehicle Stress and Usage
- Identifying Usage and Stress Patterns in a Vehicle Fleet
Target Groups
- Students and scientists in the field of automotive engineering and data science
- Engineers in the automotive industry
About the Author
Philipp Bergmeir did a PhD in the doctoral program "Promotionskolleg HYBRID" at the Institute for Internal Combustion Engines and Automotive Engineering, University of Stuttgart, in cooperation with the Esslingen University of Applied Sciences and a well-known vehicle manufacturer. Currently, he is working as a data scientist in the automotive industry.
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