Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach

Research output: Contribution to journalResearch articleContributedpeer-review


Extensive monitoring comes at a prohibitive cost, limiting Predictive Maintenance strategies for vehicle fleets. This paper presents a measurement-based virtual sensing technique where local strain gauges are only required for few reference vehicles, while the remaining fleet relies exclusively on accelerometers. The scattering transform is used to perform feature extraction, while principal component analysis provides a reduced, low dimensional data representation. This enables direct fatigue damage regression, parameterized from unlabeled usage data. Identification measurements allow for a physical interpretation of the reduced representation. The approach is demonstrated using experimental data from a sensor equipped eBike, which is made publicly available.


Original languageEnglish
Article number107554
JournalInternational Journal of Fatigue
Publication statusPublished - May 2023

External IDs

Scopus 85147995368
Mendeley b350468f-3733-3b4a-b3b7-6816d61048de
ORCID /0000-0003-3358-1545/work/142237191
ORCID /0000-0002-7431-8973/work/142250148



  • Fatigue monitoring, Maneuver identification, Predictive maintenance, Scattering transform, Soft sensing

Library keywords