Economic fatigue damage monitoring for vehicle fleets using the scattering transform

Research output: Contribution to journalConference articleContributedpeer-review


Vehicle monitoring is an important prequisite for predictive maintenance applications. Virtual sensors can be deployed to establish relationships between fatigue related quantities of interest and readily available measurement data, which reduces the costs of monitoring for vehicle fleets. This work describes a data‐driven virtual sensing approach using the scattering transform and principal component analysis. These data transformations are used to obtain a reduced representation of acceleration data, which is suitable for the identification of fatigue critical events during vehicle operation. Results of a previous study using an eBike demonstrator are summarized and the methodology is applied to experimental data of a sensor equipped light rail vehicle. In both applications, fictitious fatigue damage contributions are estimated accurately and physical interpretations of the reduced representation are found.


Original languageEnglish
Article numbere202300192
JournalProceedings in applied mathematics and mechanics : PAMM
Issue number4
Publication statusPublished - Dec 2023

External IDs

ORCID /0000-0003-3358-1545/work/143781948
ORCID /0000-0002-7431-8973/work/143783455
Mendeley 0bb1442d-d534-3554-8c67-9c089cc2c55e
Bibtex 61bd165df2484ddf8dd5362754d6500b