Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data
Bergmeir, Philipp
Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data by Philipp Bergmeir. - 1 recurso en línea (XXXII, 166 páginas 34 ilustraciones) - Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart 2567-0042 Engineering (Springer-11647) .
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.
9783658203672
10.1007/978-3-658-20367-2 doi
Aprendizaje automático
Data mining
Q325.5 / B474 2018 EB
Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data by Philipp Bergmeir. - 1 recurso en línea (XXXII, 166 páginas 34 ilustraciones) - Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart 2567-0042 Engineering (Springer-11647) .
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.
9783658203672
10.1007/978-3-658-20367-2 doi
Aprendizaje automático
Data mining
Q325.5 / B474 2018 EB