Overview
- New Approaches for Identifying Harmful Vehicle Usage Patterns
- Includes supplementary material: sn.pub/extras
Part of the book series: Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart (WRFUS)
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Table of contents (7 chapters)
Keywords
About this book
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.
Authors and Affiliations
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.
Bibliographic Information
Book Title: Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data
Authors: Philipp Bergmeir
Series Title: Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart
DOI: https://doi.org/10.1007/978-3-658-20367-2
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2018
Softcover ISBN: 978-3-658-20366-5Published: 08 December 2017
eBook ISBN: 978-3-658-20367-2Published: 01 December 2017
Series ISSN: 2567-0042
Series E-ISSN: 2567-0352
Edition Number: 1
Number of Pages: XXXII, 166
Number of Illustrations: 23 b/w illustrations, 11 illustrations in colour
Topics: Automotive Engineering, Data Mining and Knowledge Discovery, Pattern Recognition