{ "background": "The reliability of power-distribution infrastructure in many developing nations remains poorly quantified, with assessments often relying on aggregated, non-experimental data. This lack of rigorous, field-based evidence hinders targeted maintenance and investment strategies.", "purpose and objectives": "This study aimed to implement and evaluate a randomised field trial methodology to assess the failure rates and reliability of key power-distribution equipment, specifically transformers and feeder pillars, within a national network.", "methodology": "A stratified randomised sampling design was employed, selecting 450 units from operational regions across the country. Units were monitored for performance failures over a continuous operational period. Reliability was modelled using a Weibull survival analysis, with the hazard function given by $h(t) = \\frac{\\beta}{\\eta} \\left( \\frac{t}{\\eta} \\right)^{\\beta-1}$, where $\\eta$ is the scale parameter and $\\beta$ the shape parameter. Robust standard errors were calculated to account for regional clustering.", "findings": "The estimated shape parameter $\\beta$ was 1.85 (95% CI: 1.62 to 2.11), indicating an increasing failure rate over time. Transformers in coastal regions demonstrated a 34% higher hazard rate compared to those in inland areas. The median time to failure for feeder pillars was significantly lower than for transformers.", "conclusion": "The randomised trial successfully generated precise, field-derived reliability metrics, revealing distinct wear-out failure patterns and significant geographical disparities in equipment performance.", "recommendations": "Infrastructure renewal programmes should prioritise coastal networks and adopt condition-based maintenance informed by the derived Weibull parameters. The methodological framework should be institutionalised for ongoing asset management.", "key words": "reliability engineering, survival analysis, power distribution, field trial, infrastructure asset management, Weibull distribution", "contribution statement": "This paper provides the first application of a randomised field trial with survival analysis to generate empirical reliability functions for electrical