Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data [electronic resource] / by Philipp Bergmeir.
Tipo de material:
TextoSeries Wissenschaftliche Reihe Fahrzeugtechnik Universität StuttgartEditor: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Vieweg, 2018Edición: 1st ed. 2018Descripción: XXXII, 166 p. 34 illus., 11 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783658203672Tema(s): Automotive engineering | Data mining | Pattern recognition | Automotive Engineering | Data Mining and Knowledge Discovery | Pattern RecognitionFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 629.2 Clasificación LoC:TL1-483Recursos en línea: Libro electrónico
En: Springer Nature eBookResumen: 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.
| Tipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
|---|---|---|---|---|---|---|---|
| Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Acceso multiusuario
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.
UABC ; Temporal ; 01/01/2021-12/31/2023.
