Damage-based assessment of heterogeneous loads for durability target definition and test profile derivation

Abstract

Field monitoring yields heterogeneous operating-load data, including event-based histories and broadband random excitation. Durability validation requires reducing these data to traceable load targets and laboratory test profiles without losing fatigue relevance. We propose the Damage-Based Load Transfer Framework (DBLTF), which preserves domain-specific processing paths but links them through one common damage-based backbone: conditioning, load representation, lifetime modeling, Miner-type accumulation, and statistically interpretable target derivation. Two automotive case studies demonstrate the framework. In the first, fleet rear-axle torque histories are converted into damage per reference distance and into a reference-user spectrum at a prescribed coverage level. In the second, battery-housing vibration measurements are mapped to PSDs and fatigue damage spectra (FDS), consolidated into a conservative envelope, and transformed into a damage-equivalent shaker PSD. The resulting targets provide a transparent, statistically interpretable link between field usage, laboratory qualification, and durability assessment.