"background": "The reliability of water treatment infrastructure in many regions is compromised by ageing assets, inconsistent maintenance, and sparse performance data. Conventional reliability assessments often fail to adequately quantify uncertainty and integrate multi-level operational data, leading to suboptimal maintenance strategies and resource allocation. ", "purpose and objectives": "This case study aims to develop and evaluate a Bayesian hierarchical modelling framework for the reliability diagnostics of water treatment systems. The objective is to provide a robust probabilistic tool for identifying critical failure modes and informing targeted maintenance interventions. ", "methodology": "A case study methodology was employed, analysing operational and failure data from multiple treatment facilities. The core statistical model is a Bayesian hierarchical Weibull regression: T{ij \ (\, ), \ () = \0 + \1 X1ij + uj, where Tij is time-to-failure for component i in plant j, X1ij is a covariate, and uj \ N (0, \²u) captures plant-specific random effects. Posterior distributions were estimated using Markov chain Monte Carlo sampling. ", "findings": "The model identified chemical dosing systems as the least reliable subsystem, with a posterior probability exceeding 0. 85 that their mean time between failures is below the critical threshold of 90 days. The hierarchical structure revealed significant variability between plants (95% credible interval for \: 0. 42, 1. 07), indicating that facility-specific factors dominate overall reliability. ", "conclusion": "The Bayesian hierarchical model provides a powerful diagnostic framework that quantifies uncertainty and distinguishes between common and facility-specific reliability drivers. It offers a substantial improvement over deterministic or pooled analysis for infrastructure management. ", "recommendations": "Infrastructure managers should adopt probabilistic reliability models to prioritise maintenance. Immediate focus should be given to chemical dosing systems, with interventions tailored to plant
Adebayo et al. (Thu,) studied this question.