The reliability assessment of industrial machinery fleets in developing economies is often hampered by sparse, heterogeneous, and censored failure data, leading to imprecise estimates that hinder maintenance planning and capital investment. This work develops and demonstrates a Bayesian hierarchical modelling framework to robustly estimate the reliability of machinery fleets operating in Ghana, integrating disparate data sources to quantify uncertainty in key reliability parameters. A Bayesian hierarchical Weibull model is formulated, where the failure time T₈₉ Weibull (ⱼ, ₈₉) for machine i in fleet j. The shape parameter ⱼ and log-transformed scale parameter (₈₉) are modelled with hyperpriors to share information across fleets. Inference is performed using Hamiltonian Monte Carlo, with posterior credible intervals quantifying parameter uncertainty. The model application to field data from three distinct fleets indicates a strong pooling effect, significantly reducing posterior uncertainty for fleets with limited data. For one under-sampled fleet, the 95% credible interval for mean time to failure narrowed from 450, hours under a non-hierarchical model to 720, 1210 hours. The hierarchical structure effectively mitigates data scarcity by borrowing statistical strength, yielding more precise and stable reliability estimates crucial for lifecycle management. Practitioners should adopt hierarchical models for fleet reliability analysis where data is limited. Further research should integrate operational covariates and economic decision models. Reliability engineering, Bayesian statistics, hierarchical modelling, machinery maintenance, developing contexts This paper presents a novel application of Bayesian hierarchical modelling to synthesise heterogeneous field data for machinery reliability assessment in Ghana, providing a methodological framework that explicitly quantifies estimation uncertainty for improved maintenance decision-making.
Owusu-Ansah et al. (Mon,) studied this question.