Prefix Treatment and Bayesian Partial Pooling for Early-Stage Lifetime Prediction from Degradation Data

Abstract

Early-stage lifetime prediction from degradation data depends on how the observed prefix is represented. This article formulates prefix treatment as a statistical aggregation problem. Consecutive short-window extrapolations are converted into pseudo-lifetimes and regularized with Bayesian partial pooling, allowing information sharing across comparable units while preserving unit-level differences. The method is tested on gallium arsenide (GaAs) laser degradation data under a fixed six-measurement budget, with complete trajectories defining full-history pseudo lifetime references. Bayesian partial pooling yields the lowest mean absolute error, 530.9 h, compared with 550.0 h for the strongest direct early-feature baseline and 947.0 h for early global fitting. The same principle is externally evaluated on the Oxford Battery Degradation Dataset using observed 95% capacity-retention threshold lives from a fixed early cycle prefix. Bayesian partial pooling again gives the lowest leave-one-out mean absolute error, 21.0 cycles, compared with 38.9 cycles for window-median aggregation and 79.2 cycles for the strongest direct-feature baseline. Ablation results show that local-window calibration and window-specific reliability weighting carry most of the battery-side gain. The results support Bayesian partial pooling as an interpretable uncertainty-aware prefix treatment for noisy early degradation trajectories.