Unsupervised Pattern Discovery in Automotive Time Series

Pattern-based Construction of Representative Driving Cycles
1. Auflage 2022
ISBN: 978-3-658-36335-2
Verlag: Springer

Buch, Englisch, 148 Seiten, Format (B × H): 148 mm x 210 mm, Gewicht: 231 g

Reihe: AutoUni-Schriftenreihe

In the last decade unsupervised pattern discovery in time series, i.e. the problem of finding recurrent similar subsequences in long multivariate time series without the need of querying subsequences, has earned more and more attention in research and industry. Pattern discovery was already successfully applied to various areas like seismology, medicine, robotics or music. Until now an application to automotive time series has not been investigated. This dissertation fills this desideratum by studying the special characteristics of vehicle sensor logs and proposing an appropriate approach for pattern discovery. To prove the benefit of pattern discovery methods in automotive applications, the algorithm is applied to construct representative driving cycles.

Zielgruppe

Research

Autoren/Hrsg.

Noering, Fabian Kai Dietrich

Fachgebiete

• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Fahrzeugtechnik
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Introduction.- RelatedWork.- Development of Pattern Discovery Algorithms for Automotive Time Series.- Pattern-based Representative Cycles.- Evaluation.- Conclusion.

Über Autor(innen)

Fabian Kai Dietrich Noering is currently working in the technical development of Volkswagen AG as data scientist with a special interest in the analysis of time series regarding e.g. product optimization.