Industrial machinery fleet systems are crucial in mining operations, where their reliability and maintenance impact operational efficiency and safety. A Bayesian hierarchical model was employed to analyse fleet performance data from multiple mine sites, accounting for site-specific variability. Uncertainty quantification was achieved using posterior credible intervals. The analysis revealed that the proportion of machinery failures in low-impact zones (LIW) was notably lower than those in high-impact zones (HIW), indicating potential risk reduction strategies. This study demonstrated the effectiveness of Bayesian hierarchical models in monitoring and improving fleet reliability across different mine sites. Mining companies should implement targeted maintenance programmes based on site-specific conditions to optimise machinery performance. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.