arbiter
JournalsAccount

Robust Planning of Accelerated Destructive Degradation Tests: Model Discrimination, Parameter Estimation, and Lifetime Prediction

Lin Wu et al.

Applied Stochastic Models in Business and Industry2026https://doi.org/10.1002/asmb.70070article
ABDC B
Weight
0.50

Abstract

Accelerated destructive degradation tests (ADDTs) play a cr ucial role in providing timely reliability information for long‐life products. Previous research has primarily focused on identifying optimal ADDT plans for accurately predicting specific quantiles of the failure‐time distribution under use conditions, utilizing a given degradation model. However, a common challenge during the experimental design phase is the lack of confidence in selecting a model that accurately represents the underlying data. In this paper, we introduce a novel approach that specifically addresses optimal ADDT planning for model discrimination. We propose KL‐optimal design criteria to enhance the model discrimination capability of ADDTs. Moreover, we present compound DKL‐ and CKL‐optimal design criteria to ensure that ADDT plans can simultaneously achieve effective model discrimination alongside accurate parameter estimation or precise quantile prediction. We establish equivalence theorems to validate the optimality of the proposed designs. Meanwhile, we employ the particle swarm optimization algorithm to efficiently compute optimal ADDT plans. Through a practical application and sensitivity analysis, we demonstrate the effectiveness and robustness of our optimal designs. Our proposed method offers engineers with a valuable solution for developing optimal ADDT plans that meet multiple objectives.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1002/asmb.70070

Or copy a formatted citation

@article{lin2026,
  title        = {{Robust Planning of Accelerated Destructive Degradation Tests: Model Discrimination, Parameter Estimation, and Lifetime Prediction}},
  author       = {Lin Wu et al.},
  journal      = {Applied Stochastic Models in Business and Industry},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/asmb.70070},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Robust Planning of Accelerated Destructive Degradation Tests: Model Discrimination, Parameter Estimation, and Lifetime Prediction

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.