RESEARCH PAPER
A Hierarchical Bayesian Reliability Assessment Method for Vehicle Operational Tests under Zero-Failure Data
1
Chinese People's Armed Police Force Engineering University, Xi'an, , China, China
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2026-03-09
Final revision date: 2026-05-08
Acceptance date: 2026-06-01
Online publication date: 2026-07-15
Corresponding author
Yuanhong Liu
Chinese People's Armed Police Force Engineering University, Xi'an, , China, No. 1 Wujing Road, Sanqiao Subdistrict, Weiyang Di, 710086, Xi'an, China
Chinese People's Armed Police Force Engineering University, Xi'an, , China, No. 1 Wujing Road, Sanqiao Subdistrict, Weiyang Di, 710086, Xi'an, China
KEYWORDS
reliability assessmentOperational Testzero failureshierarchical BayesShifted-scaled Beta distributionMCMC algorithms
TOPICS
ABSTRACT
No-failure data are common in vehicle Operational Test (OT) because of limited test mileage and high reliability, which poses challenges for reliability assessment. This study proposes a hierarchical Bayesian method for OT reliability assessment by incorporating Developmental Test (DT) information. Under the exponential lifetime assumption, DT failure data and the engineering knowledge that OT conditions are more severe are used to construct a Shifted-scaled Beta prior for the OT failure rate. A multi-chain Metropolis-within-Gibbs MCMC algorithm with engineering constraints is developed to infer the posterior distribution and estimate the one-sided credible lower bound of the Mean miles between failures (MTBF) in OT. The case study and sensitivity analysis indicate that the proposed method can provide reasonable and interpretable reliability estimates under limited-mileage zero-failure OT conditions.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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