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.
@article{arxiv.2302.02737,
title = {Fatigue monitoring and maneuver identification for vehicle fleets using a virtual sensing approach},
author = {Leonhard Heindel and Peter Hantschke and Markus Kästner},
journal= {arXiv preprint arXiv:2302.02737},
year = {2025}
}
Comments
Accepted manuscript submitted to International Journal of Fatigue, including minor correction in 2.2 Scattering transform