Generalization bounds and sample complexity for remaining useful life prediction from complete degradation trajectories

Abstract:Data-driven remaining useful life (RUL) prediction requires complete degradation trajectories for training, yet such run-to-failure data are scarce and expensive. Practitioners currently lack principled guidance on how many failure examples suffice for a given model and accuracy target. This paper develops a sample complexity framework for RUL prediction comprising seven main results organised around three themes. First, we establish fundamental learning rates: a distribution-free generalization bound shows that the uniform deviation of the mean squared error decreases as $O(B^{2}\sqrt{p/n})$, where $p$ is the model complexity and $n$ the number of trajectories, and a minimax lower bound proves that the $\Theta(p/n)$ rate is unimprovable.} \rev{Second, we quantify how domain knowledge accelerates learning: incorporating degradation physics reduces data requirements by up to two orders of magnitude for deep networks, a Bernstein-type analysis achieves the minimax-optimal $O(p/n)$ rate under high signal-to-noise conditions, and closed-form penalties reveal when an incorrectly assumed physics model hurts rather than helps. Third, we characterise the impact of data quality: fleet variability induces an irreducible bias$-$variance tradeoff, while right-censored observations suffer an efficiency loss that depends critically on the degradation class.} Closed-form expressions are provided for exponential, power-law, and stretched-exponential degradation. \rev{Cross-domain validation against published turbofan, battery, and bearing benchmarks confirms the theoretical predictions within a factor of 2$-$3 on average. The results yield practical guidelines for planning data collection, selecting model complexity, and evaluating physics model assumptions in prognostics applications.

This manuscript has been accepted for publication in Measurement Science and Technology. The final Version of Record is available at this https URL

Machine Learning (cs.LG)

arXiv:2607.23454 [cs.LG]

(or arXiv:2607.23454v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.23454 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Meas. Sci. Technol. 37(2026) 226203

https://doi.org/10.1088/1361-6501/ae7109 Focus to learn more DOI(s) linking to related resources

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