RESEARCH PAPER
Equipment Reliability Modeling and Remaining Useful Life Prediction Based on Bayesian Small-Sample Methods
1
Army Engineering University of the PLA Shijiazhuang Campus, China
2
Army Engineering University, China
Submission date: 2025-07-16
Final revision date: 2025-10-29
Acceptance date: 2025-12-26
Online publication date: 2025-12-27
Publication date: 2025-12-27
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(1):216106
HIGHLIGHTS
- Shows model outperforms traditional ones, reducing parameter uncertainty effectively.
- Solves high-dimensional posteriors via MCMC Gibbs sampling, verifying convergence.
- Bayesian inference, using normal and inverse gamma as conjugate priors.
- Uses working cycles as life index to construct residual strength.
- Proposes a Bayesian framework for time-varying reliability estimation.
KEYWORDS
TOPICS
ABSTRACT
New equipment testing, constrained by environment and cost, yields limited reliability data, hindering accurate estimation. This paper proposes a Bayesian framework for time-varying reliability estimation and remaining life prediction of small-sample equipment. Using working cycles as the life index, it constructs residual strength of fatigue damage accumulation and derives the time-varying reliability function. To address poor parameter estimation with small samples, Bayesian inference fuses prior information with field test data. Normal and inverse gamma distributions are chosen as conjugate priors to derive posterior distributions of initial strength and working load parameters, plus maximum posterior estimates. High-dimensional posteriors are solved via Gibbs sampling in MCMC, with convergence verified. Empirical analysis shows the model outperforms traditional ones. Integrating prior and experimental data makes the posterior distribution more centralized than the prior, reducing parameter uncertainty.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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