  Extensive monitoring comes at a prohibitive cost, limiting Predictive
Maintenance strategies for vehicle fleets. This paper presents a
measurement-based soft 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.
